Job: Several postdocs, Ground Breaking Deep Learning Technology for Monitoring the Brain during Surgery with Commercialization Opportunity, University of Pittsburgh

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Kayhan just sent me the following:

Dear Igor,

I hope you are doing well.

I don't know if you remember me but we have been in contact a few times while I was a Ph.D. student at UPenn.

My lab at the University of Pittsburgh has several postdoc positions open. More specifically, I would be thankful if you could advertise this position (Link: https://kayhan.dbmi.pitt.edu/sites/default/files/JobAd.pdf) to your audience.

Best,
Kayhan

Sure Kayhan, I remember! Here is the announcement:

Ground Breaking Deep Learning Technology for Monitoring the Brain during Surgery with Commercialization Opportunity 

We are developing a clinical tool based on deep learning to automatically detect stroke during surgery and alert the surgical team to avert complications and save lives. We are uniquely positioned at the intersection of the largest health care system in the US, the University of Pittsburgh Medical Center (UPMC), and top ranked academic institutions, the University of Pittsburgh (Pitt) and the Carnegie Mellon University (CMU). Our group consists of Pitt and UPMC faculty members who have complementary expertise in machine learning and in healthcare and specifically in deep learning, clinical informatics, neurology, and surgery. We develop novel deep learning and other machine learning methods for application to challenging clinical problems. We are very well funded by NIH, NSF, industry, and internal institutional grants.  

In the current project, we are developing a clinical tool that will automatically detect stroke and other adverse events during surgery from an array of monitoring information, and provide highly accurate real time alerts to the surgical team to make course corrections during surgery. The clinical tool is to be deployed in operating rooms for monitoring surgeries and providing high quality alerts.
The successful candidate will work with us in a highly collaborative environment that spans the computer laboratory and the operating room and will gain unique and valuable experience in deep learning, development of a tool for a clinical setting, and in commercialization.  

Expected qualifications Genuinely motivated to develop and apply machine learning to clinical problems. Strong expertise in machine learning is required; expertise in statistics and experience with messy clinical data is a plus. Python fluency is required. Demonstrated ability to make meaningful contributions to projects with a research flavor is valuable.  

Experience/Abilities
• Hands-on experience building predictive models
• Experience working with diverse data types including signal and structured data; experience with text data is a plus
• Experience in programming in Python; experience in additional languages (R, C/C++) is a plus
• Aware of current best practices in machine learning
• Fluency in one of the deep learning frameworks is a plus (PyTorch or Tensorflow)
• Knowledge of statistics, including hypothesis testing with parametric and non-parametric tests and basic probability
• PhD in computer science, electrical engineering, statistics or equivalent computational / quantitative fields (exceptional MS candidates will be considered)  

The goal of this project is to develop, evaluate and commercialize a tool for automatic detection of stroke during surgery. The successful candidate will have the rare opportunity to perform cutting-edge deep learning research and participate in a commercial endeavor.  

If interested, contact Shyam Visweswaran, MD, PhD at shv3@pitt.edu and Kayhan Batmanghelich, PhD at kayhan@pitt.edu. For details of ongoing research work, visit http://www.thevislab.com/ and https://kayhan.dbmi.pitt.edu/. The University of Pittsburgh is an Affirmative Action/Equal Opportunity Employer and values equality of opportunity, human dignity, and diversity. 

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Jobs: PhD scholarship on Algorithms for Event-Driven Camera Analysis at Western Sydney University, Australia

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Paul Hurley just let me know of the following PhD scholarship

Hi Igor -- I don't know if you still put jobs/PhD scholarships on nuit blanche, but if you still do, would you mind sharing mine? It's an opportunity to build up foundational work for event-based cameras. https://www.westernsydney.edu.au/graduate_research_school/graduate_research_school/scholarships/current_scholarships/current_scholarships/scem_algorithms_for_event-driven_camera_analysis

Sure Paul ! Here is how the ad starts:

SCEM: Algorithms for Event-Driven Camera Analysis
School of Computing, Engineering and Mathematics
Scholarship code: 2019-089 

About the project
Event-driven cameras are exciting technology that do not acquire full images like traditional cameras, but record only intensity changes when they occur. The International Centre for Neuromorphic Systems at Western Sydney University has been adapting them to perform Neuromorphic space imaging. 

This PhD scholarship builds on this work to help develop the correct abstraction and a theory so as to improve knowledge extraction algorithms. It goes from modelling to algorithm testing using real data, working together with a world-class team.

What does the scholarship provide?

• Domestic candidates will receive a tax-free stipend of $30,000(AUD) per annum for up to 3 years to support living costs, supported by the Research Training Program (RTP) Fee Offset.
• International candidates will receive a tax-free stipend of $30,000(AUD) per annum for up to 3 years to support living costs. Those with a strong track record will be eligible for a tuition fee waiver.
• Support for conference attendance, fieldwork and additional costs as approved by the School.

International candidates are required to hold an Overseas Student Health Care (OSHC)(opens in new window)insurance policy for the duration their study in Australia. This cost is not covered by the scholarship.

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Jobs: 2 PhD and RA positions at University of Luxembourg

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Kumar also sent me the following announcements for different positions:

Dear Igor,

I was wondering if you could post on Nuit-Blanche the announcement of the following Ph.D./R.A. positions at SnT, University of Luxembourg on signal processing for next-generation radar systems.

• Two Ph.D. in signal processing for next-gen radar networks in mmWave & beyond: http://emea3.mrted.ly/28n6x
• One RA position in Signal Processing for Extremely High-Frequency Radar: http://emea3.mrted.ly/28n6x
• One RA position in performance optimization methodologies for MIMO radar networks: http://emea3.mrted.ly/28f97

Thanks!

--

Regards,

Kumar Vijay Mishra

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Jobs: Several Postdocs in machine learning and information processing, LIONS, EPFL, Switzerland

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Volkan just let me know of several openings:

Dear Igor,

I hope this email finds you well.

I would really appreciate it if you could advertise my postdoc position at NB.

best,
=====

Prof. Volkan Cevher

Laboratory for Information and Inference Systems
http://lions.epfl.ch

Here is the announcement:

The LIONS group at EPFL has several openings for postdoctoral fellows for research in machine learning and information processing. Please see our research interests at
https://lions.epfl.ch.

We are looking for candidates with a strong theory background in machine learning, discrete optimization, information theory, statistics, compressive sensing, or other related areas. Strong coding skills is a big plus.
There are two positions that revolve around the following two topics:

1) Bayesian optimization, bandits, and reinforcement learning
We seek to develop online algorithms for Bayesian optimization, as well as related problems such as multi-armed bandits, level-set estimation, and reinforcement learning. The algorithms will be characterized theoretically, and also tested in real-world applications including automated hyperparameter optimization with neural networks and personalized education.
2) Discrete optimization and submodularity with applications to subsampling
We seek to develop techniques for discrete optimization, with submodularity and related concepts playing a key role. These techniques will be targeted at the application of using data in order to optimally subsample for the purpose of performing a given task, such as estimation in compressive sensing or classification in machine learning. Specific applications will also be explored, including medical resonance imaging (MRI) with multiple coils.
3) Continuous optimization theory and methodology
We seek to develop gradient and linear minimization oracle based algorithms for convex and non-convex problems. In particular, we are interested in the marriage of online and offline optimization, universal adaptation, and storage optimal solutions to difficult training problems that range from semidefinite programming to neural network training.

LIONS provides a stimulating, collaborative and fun research environment with state-of-the-art facilities at EPFL. Personal initiative and independent research tasks related with the candidate’s interests are also encouraged.
The working language at EPFL is English.
Candidates should have or be close to finishing a PhD degree in electrical engineering, computer science, applied mathematics, or a related field. Candidates should send their CV, a research statement outlining their expertise and interests, any supplemental information, and a list of at least three references with full contact information to the LIONS Lab Administrator:
Gosia Baltaian (gosia.baltaian@epfl.ch)
======

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Job: Postdoctoral Researcher in Small Data Deep Learning and Explainable Machine Learning, Livermore, CA

Bhavya just sen me the following:

Hi Igor, 

I would like to ask you a favor. We are looking for a Postdoctoral Researcher interested in small data deep learning and explainable machine learning. I was wondering whether it is possible to list the opening on your blog. Information on the available position is below.
We are looking for a Postdoctoral Researcher with expertise in statistics, machine learning, convex/non-convex optimization and/or uncertainty quantification. Postdoctoral Researcher will support ongoing efforts concerned with small-data deep learning and related topics, such as, transfer learning, generative modeling, self-supervised or unsupervised learning, and explainable ML. This position is in the Computation Directorate within the Center for Applied Scientific Computing (CASC) Division at Lawrence Livermore National Lab, Livermore, CA.
Essential Duties

• Research, design, implement and apply a variety of advanced data science methods in multiple application areas (such as material science, high energy physics, predictive medicine, cybersecurity) in a collaborative scientific environment.

• Document research by publishing papers at conferences/journals such as NIPS, ICML, ICLR, IJCAI, AAAI, AISTATS, ACL, CVPR, JMLR or similar.

Qualifications

• Ph.D. in statistics or computer science or a related field.

• Experience in modern machine learning environments (TensorFlow, PyTorch, etc.).

• Proficiency in one or more of the following machine learning areas: deep learning, reinforcement learning, and Bayesian nonparametric.

• Knowledge of C/C++, Python.

If interested, please contact me directly at kailkhura1@llnl.gov.
Regards,
Bhavya

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CSJob: PhD and Postdoc positions KU Leuven: Optimization frameworks for deep kernel machines

Johan let me know of the following positions in his group:

Dear Igor,
could you please announce this on nuit blanche.
many thanks,
Johan

Sure thing Johan !

PhD and Postdoc positions KU Leuven: Optimization frameworks for deep kernel machines
The research group KU Leuven ESAT-STADIUS is currently offering 2 PhD and 1 Postdoc (1 year, extendable) positions within the framework of the KU Leuven C1 project Optimization frameworks for deep kernel machines (promotors: Prof. Johan Suykens and Prof. Panos Patrinos).
Deep learning and kernel-based learning are among the very powerful methods in machine learning and data-driven modelling. From an optimization and model representation point of view, training of deep feedforward neural networks occurs in a primal form, while kernel-based learning is often characterized by dual representations, in connection to possibly infinite dimensional problems in the primal. In this project we aim at investigating new optimization frameworks for deep kernel machines, with feature maps and kernels taken at multiple levels, and with possibly different objectives for the levels. The research hypothesis is that such an extended framework, including both deep feedforward networks and deep kernel machines, can lead to new important insights and improved results. In order to achieve this, we will study optimization modelling aspects (e.g. variational principles, distributed learning formulations, consensus algorithms), accelerated learning
schemes and adversarial learning methods.
The PhD and Postdoc positions in this KU Leuven C1 project (promotors: Prof. Johan Suykens and Prof. Panos Patrinos) relate to the following  possible topics:
-1- Optimization modelling for deep kernel machines
-2- Efficient learning schemes for deep kernel machines
-3- Adversarial learning for deep kernel machines
For further information and on-line applying, see
https://www.kuleuven.be/personeel/jobsite/jobs/54740654" (PhD positions) and
https://www.kuleuven.be/personeel/jobsite/jobs/54740649" (Postdoc position)
(click EN for English version).
The research group ESAT-STADIUS http://www.esat.kuleuven.be/stadius at the university KU Leuven Belgium provides an excellent research environment being active in the broad area of mathematical engineering, including data-driven modelling, neural networks and machine learning, nonlinear systems and complex networks, optimization, systems and control, signal processing, bioinformatics and biomedicine.

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PhD and Postdoc positions KU Leuven (ERC Advanced grant E-DUALITY)

Johan just asked me the following:

Dear Igor,

could you please announce these vacancies on Nuit Blanche.

Best regards,
Johan

Sure Johan !

---------------------------------------------

The research group KU Leuven ESAT-STADIUS is currently offering 3 PhD and 3 Postdoc (1 year, extendable) positions within the framework of the ERC (European Research Council) Advanced Grant E-DUALITY http://www.esat.kuleuven.be/stadius/E (PI: Johan Suykens) on Exploring Duality for Future Data-driven Modelling.
Within this ERC project E-DUALITY we aim at realizing a powerful and unifying framework (including e.g. kernel methods, support vector machines, deep learning, multilayer networks, tensor-based models and others) for handling different system complexity levels, obtaining optimal model representations and designing efficient algorithms.
The research positions relate to the following possible topics:

1. Duality principles
2. Multiple data sources and coupling schemes
3. Manifold learning and semi-supervised schemes
4. Optimal prediction schemes
5. Scalability, on-line updating, interpretation and visualization
6. Mathematical foundations
7. Matching model to system characteristics

For further information and on-line applying, see https://www.kuleuven.be/personeel/jobsite/jobs/54681979" (PhD positions) and https://www.kuleuven.be/personeel/jobsite/jobs/54681807" (Postdoc positions) (click EN for English version).
The research group ESAT-STADIUS http://www.esat.kuleuven.be/stadius at the university KU Leuven Belgium provides an excellent research environment being active in the broad area of mathematical engineering, including data-driven modelling, neural networks and machine learning, nonlinear systems and complex networks, optimization, systems and control, signal processing, bioinformatics and biomedicine.

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CSjob: Research Fellow in Machine Listening, University of Surrey, UK

Mark sent me the following a few days ago:

Dear Igor, 

I thought that Nuit Blanche readers may be interested in this job opportunity.I will be at ICASSP in Calgary next week, in case anyone would like more details. Our "Making Sense of Sounds" project will have presentations  related to this post, including in poster session AASP-P3  ("DCASE I", Wednesday, April 18, 08:30 - 10:30) and lecture Session  AASP-L6 ("DCASE II", Thursday, April 19, 13:30-15:30, where I co-chair). 

Best wishes, Mark

Sure Mark !

============================================================
Research Fellow in Machine Listening
University of Surrey, UK
Salary: GBP 30,688 to GBP 38,833 per annum
Closing Date: 1 May 2018 (23:00 BST)
https://jobs.surrey.ac.uk/021518 

Applications are invited for a Research Fellow in Machine Listening to work full-time on an EPSRC-funded project "Making Sense of Sounds", to start as soon as possible, for 9.75 months until 13 March 2019. This project is investigating how to make sense from sound data, focussing on how to allow people to search, browse and interact with sounds. The candidate will be responsible for investigating and developing machine learning methods for analysis of everyday sounds, leading to new representations to support search, retrieval and interaction with sound. 

The successful applicant is expected to have a PhD or equivalent in electronic engineering, computer science or a related subject, and is expected to have significant research experience in audio signal processing and machine learning. Research experience in one or more of the following is desirable: deep learning; blind source separation, blind de-reverberation, sparse and/or non-negative representations, audio feature extraction. 

The project is being led by Prof Mark Plumbley in the Centre for Vision Speech and Signal Processing (CVSSP) at the University of Surrey, in collaboration with the Digital World Research Centre (DWRC) at Surrey, and the University of Salford. The postholder will be based in CVSSP and work under the direction of Prof Plumbley and Co-Investigators Dr Wenwu Wang and Dr Philip Jackson. For more about the project see:
http://cvssp.org/projects/making_sense_of_sounds/ 

CVSSP is an International Centre of Excellence for research in Audio-Visual Machine Perception, with 125 researchers, a grant portfolio of £20M. The Centre has state-of-the-art acoustic capture and analysis facilities enabling research into audio source separation, music transcription and spatial audio. Audio-visual compute includes 700 cores and a 50GPU machine learning cluster with 500TB of online storage. Informal enquires are welcome, to: Prof Mark Plumbley (m.plumbley@surrey.ac.uk), Dr Wenwu Wang (w.wang@surrey.ac.uk), or Dr Philip Jackson (p.jackson@surrey.ac.uk).
For more information and to apply online, please visit:
https://jobs.surrey.ac.uk/021518
We acknowledge, understand and embrace diversity.
============================================================
--
Prof Mark D Plumbley
Professor of Signal Processing
Centre for Vision, Speech and Signal Processing (CVSSP)
University of Surrey, Guildford, Surrey, GU2 7XH, UK
Email: m.plumbley@surrey.ac.uk
===========================================================
LVA/ICA 2018
14th International Conference on Latent Variable Analysis and Signal Separation
July 2-6, 2018, University of Surrey, Guildford, UK
http://cvssp.org/events/lva-ica-2018===========================================================

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Job: Ingénieur R&D Machine learning/Deep learning, INA, France

Louis menioned to me this job announcement in French recently:

L’Institut national de l’audiovisuel (http://institut.ina.fr) recrute pour son Département Recherche et Innovation un Ingénieur R&D en CDI, sur les technologies de Machine Learning, et plus particulièrement de Deep Learning, appliquées à la vidéo, l’image, l’audio et/ou le texte, pour la valorisation du patrimoine audiovisuel, et plus généralement pour la conception et l’expérimentation de nouveaux usages dans le domaine des médias. En lien avec les chercheurs et doctorants du Département et avec les services opérationnels de l’INA, vous aurez pour mission de porter cette problématique et de participer aux projets de recherche et d’innovation de l’Institut, dans un cadre à la fois industriel et académique. A terme, vous pourrez être amené à participer à l’encadrement des doctorants.
Diplôme requis : doctorat.
Compétences : 

• Machine Learning, Deep Learning : aspects formels (par ex. CNN, RNN, LSTM, GAN) et frameworks état de l’art (par ex. PyTorch, Tensorflow, Keras) 

• Développement informatique : Python, C/C++, Java 

• Publications scientifiques 

• Intérêt pour les applications opérationnelles des résultats de recherche 

• Intérêt pour le monde de l’audiovisuel et des médias, pour les Sciences Humaines et Sociales et les Humanités Numériques 

Contact : jcarrive@ina.fr et obuisson@ina.fr

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Job: Several Tenured and Tenure-Track Faculty positions in robotics at ENSTA ParisTech, France

Natalia sent me the following the other day:

Tenured and Tenure-Track Faculty positions in robotics at ENSTA ParisTech
The Computer Science and System Engineering department (U2IS) at ENSTA ParisTech is opening several tenured and tenure-track faculty positions in the field of robotics.  

== Position description ==
The candidates will be recruited in the Autonomous Systems and Robotics research team, inside the U2IS department. ASR dedicates itself to the development of technological systems with strong autonomy and high dependability, focusing in particular on learning, perception, navigation, human-robot interaction for assistive robotics and intelligent vehicles. The candidates will lead research and innovation activities on one or several of the following themes:

• perception, computer vision, image processing, human-robot interaction
• machine learning, developmental and cognitive robotics
• navigation, SLAM, localization, planning and control 
• systems architecture for autonomous vehicles.

These activities should be conducted in the application areas of intelligent vehicles, automation, assistive/service robotics or defense and security, in link with the other members of the team. In particular, candidates could be integrated in the joint ENSTA ParisTech-INRIA FLOWERS team. The candidates are expected to participate in the development of partnerships, collaborations and projects in his/her domain, particularly in partnership with industry. Inquiries about the scientific context of the position can be directed to David Filliat (david.filliat@ensta-paristech.fr) or Bruno Monsuez (bruno.monsuez@ensta-paristech.fr).

Faculty duties include teaching at the graduate and undergraduate levels, research, and supervision of student research. Basic knowledge, or willingness to learn French language are required as part of the teaching will be in French. Candidates must have the ability to develop a leading research program with a focus on technology development and translation into concrete applications such as robotics or intelligent vehicles.

== About ENSTA ==
ENSTA ParisTech is one of the most renowned French institutes of engineering education and research (Grande Ecole). Located in Palaiseau, it offers graduate level scientific education, excellent research facilities and a broad international network. It is a founding member of Paris-Saclay University, a federal university composed of 19 institutions (Universities, Grandes Ecoles, Research organisms). Large companies have also settled their research center in this area so that by 2020, this scientific cluster, the largest in France, will gather up to 15% of French research.

== Application ==
Requirements for applying are: possession of a doctoral university degree, excellent skills in teaching and research, a strong publication record, and have experience with conducting research projects. Candidates for tenured position should possess the "Habilitation à diriger des Recherches" or comparable research experience.
The candidates will be expected to conduct high quality research, teaching, and secure competitive external funding.
The complete application package includes:

• a curriculum vitae including a list of publications;
• a research statement;
• a teaching statement including a list of lectures the candidate could teach;
• the names and email addresses of three references.

Applications integrated into a single pdf file should be sent to David Filliat (david.filliat@ensta-paristech.fr) and Bruno Monsuez (bruno.monsuez@ensta-paristech.fr). The application deadline is March 30th, 2018.
== Links ==
ENSTA ParisTech: http://www.ensta.fr/en/Official position description (in French): http://www.ensta.fr/fr/decouvrir-ensta/ensta-paristech-recruteAutonomous Systems and Robotics team: http://asr.ensta.frENSTA ParisTech - INRIA FLOWERS team: http://flowers.inria.fr

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CSJob: PhD Position for Signal Processing for Energy Harvesting Sensor Networks, Uppsala University, Sweden

Ayça just sent me the following:

Dear Igor,

We have recently opened a PhD position in our group that can be of interest to your subscribers. The details are below. I was wondering whether it would be possible to share it through Nuit Blanche?

Best Regards,
Ayca Ozcelikkale
https://sites.google.com/site/aycaozcelikkale

Sure. Ayça !

PhD Position for Signal Processing for Energy Harvesting Sensor Networks
Project Description
In recent years, energy harvesting (EH) solutions have become an emerging paradigm for powering up future wireless sensing systems. As such, energy harvesting constitutes a key enabling technology for Internet-of-Things (IoT) applications and Wireless Sensor Networks (WSN) including smart homes and smart factories. This position focuses on providing signal processing solutions for such energy harvesting wireless sensor networks. Modern machine learning techniques and optimization approaches will be important ingredients of the work.
Position Description
This is a full-time position where you will be employed by Uppsala University. Your main responsibility will be to pursue your own doctoral studies. The PhD position is for four years, extendable to a maximum of five years, including departmental duties at a level of at most 20% (typically teaching).
Application deadline: 15 February 2018
More information and application instructions:
https://www.uu.se/en/about-uu/join-us/details/?positionId=186914

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Job: Machine Learning and Computer Vision Engineer, Scortex, Paris, France

 Christophe let me know of a job opportunity at Scortex.io, the announcement is here:

About

Scortex deploys artificial intelligence in the heart of factories.
We help our customers take the next big leap in smart automation thanks to our Quality Intelligence Solution.
Our platform enables manufacturing companies to take control of their quality:

• Automate visual inspection tasks
• Monitor key quality data in real time through our intuitive platform 
• Improve production process by consolidating production knowledge.

Thanks to our proprietary deep learning platform, we provide a state-of-the-art performance and robust vision solution for quality intelligence.
What you will do

As a proactive member of the machine learning and computer vision team, your work will include a varied range of challenges:
explore various state of the art techniques to help solve tasks currently unbeaten by computers;

• stay on the bleeding edge of research and participate actively in the community;
• design, develop and implement supervised and unsupervised models with extremely constraining requirements not only on accuracy, but also on real-time execution, fast and scalable training processes and minimal annotation levels;
• help improve our pipelines of data acquisition, training and inference.

What we are looking for

• In-depth knowledge of deep learning techniques applied to computer vision: deep convolutional networks, autoencoders, image (pre)processing, regularization;
• Proficient knowledge of both supervised and unsupervised machine learning techniques : clustering, object detection, generative models, dimensionality reduction;
• Understanding of standard computer vision techniques : filtering, transformations, descriptors and detectors;
• Knowledge and understanding of the mathematics underlying all of the above : probability and statistics, optimization, linear algebra, numerical computation;
• Proven experience with at least one machine learning framework (bonus points for Keras or Tensorflow);
• Good programming and software engineering skills;
• Experience with the unix environment.
• 
  

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Job: Research engineer position in the DREAM project at ISIR, Sorbonne-Université, Paris, France.

Natalia let me know of the following job position

Research engineer position in the DREAM project at ISIR, Sorbonne-Université, Paris, France.

Job position available immediately and for 1 year (may be extended).

The DREAM European project (http://www.robotsthatdream.eu/) is focused on the bootstrap of a developmental process allowing a robot to learn about its environment and the objects it contains.

We are looking for highly motivated candidates with a strong experience in developing software for robotics, in particular on the ROS middleware. The recruited engineer will be in charge of the development and deployment of the ROS modules supporting the DREAM cognitive architecture. He/she will also help the partners to integrate their work into the cognitive architecture and will work on the validation experiments of the project. Programming skills in modern C++ and python are expected. The position involves robotics experiments that will be done on Baxter, PR2 and Pepper robots. The position may be extended later on to more than one year.

The position is located in the Institute of Intelligent Systems and Robotics (ISIR, http://www.isir.upmc.fr), Paris, France. ISIR belongs to Sorbonne Université which is among the top ranked French universities (http://sorbonne-universite.fr/en).

Speaking or understanding french is not required.

To apply, please send a CV, letter of motivation (max 2 pages), and a list of three references via e-mail to stephane.doncieux@upmc.fr. Please put [DREAM engineer application] in the subject of the mail. Review of applicants will begin immediately, and will continue until the position is filled.

Best regards,

Stephane Doncieux

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CSjob: Postdoc for research in reservoir computing, University of St.Gallen, Switzerland

Stéphane just let me about this Postdoc opportunity:

Dear Igor,

...

I am also contacting you because my friend Juan-Pablo Ortega is offering a postdoc position at St Gallen University (Switzerland) on very exciting new trends in Machine Learning, based on Reservoir Computing. Do you think it could be appropriate to post a link to this job announcement on your blog Nuit Blanche (see pdf attached) ? Juan Pablo’s web page is here: http://juan-pablo-ortega.com/

All the best,

Stephane

National Physical Laboratory

Data Science Division

Sure Stephane, here is the announcement:

At the Faculty of Mathematics and Statistics of the University of St. Gallen a new ASSISTANT / POSTDOCTORAL POSITION for research in reservoir computing will be filled starting April 1st , 2018 or later.
This position is funded by the Swiss National Science Foundation project entitled “Novel Ar chitectures for Photonic Reservoir Computing”. Research will be conducted under the su pervision of Prof. Juan-Pablo Ortega (see http://juan-pablo-ortega.com for an overview of his  current research agenda) and in collaboration with the photonics group of the IBM Research  labs in Zurich. The tentative duration of the contract is 30 months. The successful candidate will become part of a young research team, with a strong interest  for the interplay between dynamical systems, machine learning, and statistical modeling, as  well as for applications of those techniques to financial econometrics and physiological signal  treatment. The group is located at the Faculty of Mathematics and Statistics of the University  of St.Gallen (http://www.mathstat.unisg.ch).
Applications for the position should be sent by e-mail to Mrs. Margit Albers (math- stat@unisg.ch) no later than March 1st, 2018.
Candidate Profile: - Ph.D. in a strongly quantitative subject: Mathematics, Computer Science, Statistics, Physics, Engineering... - Strong background in dynamical systems and in both deterministic and statistical modeling - Interest for machine learning and optimization - Knowledge in financial econometrics and/or signal treatment is a plus - Good programming skills (Matlab, R, Python, C,...) are required The application package must contain: - Motivation letter - Complete Curriculum vitae - Two recommendation letters Duties: - Research activity in the context of the project “Novel Architectures for Photonic Reservoir Computing”. - Some teaching support at the Faculty of Mathematics and Statistics at various levels.

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CSjob: One PhD and one Postdoc Position, Low-Rank Matrix Approximations

Nicolas just let me know of this opportunity:

PhD/Postdoc Positions, Low-Rank Matrix Approximations

The research group of Nicolas Gillis at the University of Mons (Belgium) is seeking highly qualified candidates for one doctoral and one postdoctoral position funded by the ERC (European Research Council).
This project, entitled Constrained Low-Rank Matrix Approximations - Theoretical and Algorithmic Developments for Practitioners, focuses on the theory, algorithms and applications of low-dimensional representations of high-dimensional data based on low-rank matrix approximations; see https://sites.google.com/site/nicolasgillis/coloramap/overview for a description of the project and the objectives. The successful candidate will have the flexibility to choose a topic within the range of this project.
Your profile is a MSc/PhD in applied mathematics, mathematics, computer science, or a related discipline. You must have an expertise in at least one of these fields: optimization, computational complexity, numerical linear algebra, data mining and machine learning, hyperspectral imaging. You should have excellent programming skills in a numerical language (such as Matlab), and good communications skills, both written and oral, in English.
The positions remain open until they are filled. The starting date is flexible (ideally in September 2018).
Please see https://sites.google.com/site/nicolasgillis/coloramap/ for more details.

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Job: Two Postdocs: Large-scale, storage-optimal continuous optimization / Convex relaxations for discrete optimization problems, EPFL, Switzerland

Volkan just sent me the following

Dear Igor,

It has been a while. ...May I also advertise some postdoc positions at nuit-blanche as well? I would really appreciate the shoutout... 

Sure Volkan, here they are: 

ERC funded positions (2) for postdoctoral researcher
Large-scale, storage-optimal continuous optimization
Convex relaxations for discrete optimization problems

Laboratory for Information and Inference Systems (LIONS)
Ecole Polytechnique Fédérale de Lausanne (EPFL)
lions.epfl.ch

The LIONS group is looking for postdoctoral candidates with solid experience in developing optimization theory and algorithms. Knowledge of continuous relaxations for discrete submodular optimization problems is a big plus.

The ideal candidate should have a research profile in applied mathematics, computer science, electrical engineering, or a related field. The initial appointment is for 1 year, and can be extended up to three years.

Candidates should send their CV, a research statement outlining their expertise and interests, any supplemental information, and a list of at least three references with full contact information to the LIONS Lab Administrator:
Gosia Baltaian (gosia.baltaian@epfl.ch)
Review of applications begins immediately, and continues until the position is filled. Short-listed candidates may be invited for an interview.

For our research interests, please further see

https://lions.epfl.ch/research



----
best,
-------
Prof. Volkan Cevher
Laboratory for Information and Inference Systems
http://lions.epfl.ch

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Job: Internship position (Spring/Summer 2018 + PhD position follow-up), IFPEN, France

Laurent just sent me the following the other day:

Dear Igor


Following the high quality applications (and practical results in only 3 months) after your post on:
http://nuit-blanche.blogspot.fr/2016/12/internship-signal-and-image.html
let me share with you the follow-up internship (5 month):


Analysis and prediction of wavelet and filter-bank frames performance for machine learning 

http://www.laurent-duval.eu/lcd-2018-intern-transform-classification.html 

and full PDF description 

http://www.laurent-duval.eu/Documents/IFPEN_2018_SUBJ_Internship-Multiscale-complex.pdf 

with a subsequent PhD proposal (Data characterization and classification with invariant multiscale features)


Objectives and context follow (more at the webpage)


We wish to study large datasets of experimental data (e.g. physico-chemical spectral signals, microscopy or geophysical subsurface images) toward clustering, classification and learning. When data satisfy regularity properties, they often admit sparse or compressible representations in a judicious transformed domain: a few transformed coefficients provide accurate data approximation. Such representations, like multiscale or wavelet transforms, are beneficial to subsequent processing, and they form the core of novel data processing methodologies, such as Scattering networks/transforms (SN) or Functional Data Analysis (FDA). Due to the variety of such transforms, without prior knowledge, it is not evident to find the most suitable representation for a given set of data. The aim of this subject is to investigate potential relations between transform properties and data compressibility on the one hand, and classification/clustering performance on the other hand, especially with respect to the robustness to shifts/translations or noise in data features, with matters in experimental applications. Rooting on a recent work, the first objective is to develop a framework to allow the use of different sparsifying transformations (bases or frames of wavelets and multiscale transformations) at the input of reference SN algorithms. This will permit to evaluate the latter on a variety of experimental datasets, with the aim of choosing the most appropriate, both in terms of performance and usability, since the redundancy in transformations may hinder their application to large datasets. A particular interest could be laid on complex-like transformations, that may improve either the sparsification or ”invariance properties” in the transformed data. Their importance has been underlined recently for deep convolutional networks. Then, starting from real data, the trainee will develop realistic models reproducing the expected behaviors in the data, for instance related to shifts or noise. Finally, the relative clustering/classification performances will be assessed with respect to different trans- formation choices, and their impact on both realistic models and real data. A particular interest could be laid on either transform properties (redundancy, frame bounds, asymptotic properties) or the resulting data multiscale statistics.


Hoping you can still disclose this information with your venture at http://www.lighton.io/


Best

Sure Laurent ! The more qualified people around Paris, the better (a tide lifts all boats).

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CSjob: Multimedia / Research Scientist or Principal Research Scientist - Signal Processing, MERL, Massachusetts, USA

Petros just sent me the following:

Dear Igor, 

I hope you are doing well. We are excited to have a new opening in the Computational Sensing Team at MERL. I would appreciate it if you can post this on your blog, or otherwise disseminate as you see fit, and encourage anyone you think might be a good candidate to apply. Posting and application link is also here: http://www.merl.com/employment/employment.php#MM29

Thanks!

Petros

Sure Petros, here is the job ad:

MM29 - Multimedia / Research Scientist or Principal Research Scientist - Signal Processing
MERL's Computational Sensing Team is seeking an exceptional researcher in the area of signal processing, with particular emphasis on signal acquisition and active sensing technologies. Applicants are expected to hold a Ph.D. degree in Electrical Engineering, Computer Science, or a closely related field.
The successful candidate will have an extensive signal processing background and familiarity with related techniques, such as compressive sensing and convex optimization. Specific experience with wave propagation or PDE constrained inverse problems, or with signal acquisition via ultrasonic, radio, optical or other sensing or imaging modalities, is a plus. Applicants must have a strong publication record in any of these or related areas, demonstrating novel research achievements.
As a member of our team, the successful candidate will conduct original research that aims to advance state-of-the-art solutions in the field, with opportunities to work on both fundamental and application-motivated problems. Your work will involve initiating new projects with long-term research goals and leading research efforts.
MERL is one of the most academically-oriented industrial research labs in the world, and the ideal environment to thrive as a leader in signal processing. MERL strongly supports, encourages, and values academic activities such as publishing and presenting research results at top conferences, collaborating with university professors and students, organizing workshops and challenges, and generally maintaining an influential presence in the scientific community.

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Job: Faculty position, ECE, Ohio State

Phil just sent me the following:

Hi Igor 

....
I wanted to let you know that our department (ECE at Ohio State) has an open faculty position covering the areas of image processing, MRI/fMRI, brain imaging, and neuroscience. The official job posting can be found at https://ece.osu.edu/about/employment
I'd be grateful if you considered posting this on your excellent Nuit Blanche blog.
Thanks,
Phil
--
Phil Schniter
Professor, The Ohio State University
 http://www.ece.osu.edu/~schniter

Sure Phil !

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