There are 2 new PhD vacancies in the field of speech- and text
anonymization in the medical domain in Berlin, Germany.
Both positions are in the “Medinym” project, where the department of Speech
and Language Technologies of German Research Center for Artificial
Intelligence works jointly with the Quality and Usability Labs of Berlin
Institute of Technology.
In both departments, we’re looking for a Researcher or Junior Researcher
level, offer a 2 years contract with optional prolongation and PhD
perspective.
*Application deadline: Dec 23, 2022*
More details and links:
DFKI:
https://jobs.dfki.de/en/internal/vacancy/en-researcher-m-w-d-in-506968.html
TU Berlin: https://tub.stellenticket.de/de/offers/157309/?locale=en
Please circulate upon potentially interested. In case of questions pls
contact me, I’m happy to help and forward you with respect to both
vacancies.
INRIA Lille
CEA et Sorbonne Univeristé
Aalto University
The Aalto Robot Learning research group operates in the intersection of
artificial intelligence and robotics. We focus on developing methods for
reinforcement learning, robotic manipulation, decision making under partial
observability, imitation learning, and decision making in multi-agent
systems. For more information, please see
https://www.aalto.fi/en/department-of-electrical-engineering-and-automation/robot-learning.
The Robot Learning research group is seeking a talented PhD student to
develop novel reinforcement learning and planning methods. The methods will
be used for automating hardware acceleration generation. The project is a
collaboration with the hardware accelerator design group at Tampere
University. The project aims to create a novel automated accelerator
architecture generation tool based on reinforcement learning that starts
from high-level program descriptions and generates complete compiler
programmable accelerators with desired qualities such as optimized
energy-efficiency within a set computational latency.
The developed methods may be based on one of our focus areas including, but
not limited to, reinforcement learning and planning under uncertainty. The
exact direction of the research is chosen depending on your experience and
interests. Please relate clearly to some of the research topics in your
Letter of Motivation.
Outstanding researchers from the areas of Machine Learning, AI, and
Robotics and related areas including Reinforcement Learning, Control
Engineering, Computer Vision, Statistics & Optimization, or Mathematics &
Physics are welcome to apply. The candidate is expected to conduct
independent research and at the same time contribute to the topics listed
above. Successful candidates will be furthermore given the opportunity to
work with undergraduate, and M.Sc. students.
The research group collaborates with research groups in hardware design,
construction, computer vision, robotics, mobile heavy machines, human-robot
interaction, reinforcement learning, imitation learning, multi-agent
systems, under-water vehicles, and robot motion planning. Moreover, one of
the PhD students in the research group is located at the Intelligent
Autonomous Systems institute at TU Darmstadt, Germany, so there will be
ample opportunities for international collaboration.
WE OFFER
The position will be filled for a period of 4 years (2 + 2). The starting
date is in January 2023 or as mutually agreed. The salary will be based on
both the job requirements and the employee’s personal performance in
accordance with the salary system of Finnish universities. The starting
salary for a PhD student is approximately 2600 EUR/month.
We offer a wide range of staff benefits, such as occupational health care,
flexible working hours, excellent sports facilities on campus and several
restaurants and cafés on campus with staff discounts. The position is
located at the Aalto University Otaniemi campus which can be easily reached
by public transport.
HOW TO APPLY
Please submit your application through our online recruitment system at
https://www.aalto.fi/en/open-positions/phd-student-position-in-reinforcement-learning-and-planning-for-hardware-accelerator-design
The closing date for applications is December 18th, 2022 (23:59 EEST
(GMT+3)).
Please write your application and all the accompanying documentation in
English and attach them in PDF format. Please attach only the following
documents to your application:
* A letter of motivation describing your research interests and how the
research fits to the Robot Learning research group (max. 1 page)
* Curriculum vitae (including the contact details of two referees)
* A list of publications as a part of the curriculum vitae
* PDF copy of your MSc and BSc degree certificates, including transcripts
of all MSc and BSc university records (grades and courses) and their
English translations (Finnish and Swedish certificates are also accepted)
Please note that our recruitment system allows max 5 attachments, so please
combine the copies of certificates and transcripts in one PDF, if necessary.
ADDITIONAL INFORMATION
For further information, please contact Assistant Professor Joni Pajarinen,
joni.pajarinen@aalto.fi. Additional information in recruitment process
related questions, please contact HR Coordinator Camilla Hanganpää,
camilla.hanganpaa@aalto.fi.
ABOUT AALTO UNIVERSITY, HELSINKI, AND FINLAND
Aalto University (aalto.fi) is located in Finland. Finland is among the
best countries in the world according to many quality of life indicators,
including being the happiest country in the world (UN study 2018 and The
World Happiness Report 2022). In addition to a computing cluster at Aalto
University and computing clusters in Finland available to the research
group, Finland has Europe’s fastest supercomputer LUMI. Aalto University is
the foremost university in Finland in Engineering, Design and Business.
Less than a 15 minutes metro ride away from center of Helsinki, capital of
Finland, Aalto offers access to rich cultural and social life to help
maintain healthy work-life balance.
Aalto University is a community of bold thinkers where science and art meet
technology and business. We are committed to identifying and solving grand
societal challenges and building an innovative future. Aalto has six
schools with nearly 11 000 students and a staff of more than 4000, of which
400 are professors. Our main campus is located in the Helsinki metropolitan
area, Finland. Diversity is part of who we are, and we actively work to
ensure our community’s diversity and inclusiveness in the future as well.
This is why we warmly encourage qualified candidates from all backgrounds
to join our community.
Centre d’Écologie Fonctionnelle et Évolutive
University of Bari
*#Call for #applications – #Research #Fellowship at Università degli Studi
di Bari, Bari (ITALY)*
Closing date for applications: 09/Dec/2022
Keywords: #artificialintelligence, #deeplearning, #healthcare, #wellbeing.
URL for applications:
https://reclutamento.ict.uniba.it/assegni-di-ricerca/concorsi/2022-pr-01.82
Applications are invited for a 18-month fellowship (assegno di ricerca) for
conducting research within the project “Artificial intelligence techniques
for monitoring adverse reactions of anti-covid vaccines”, funded by POC
PUGLIA FESR-FSE 2014/2020- azione 10.4 “Interventi volti a promuovere la
ricerca e per l’Istruzione Universitaria” –“RIPARTI” (Regione Puglia). The
Research Fellow will work on Artificial intelligence techniques for
monitoring adverse reactions of anti-covid vaccines. The Research Fellow
will spend 10 months in Miraclesrl (https://lnkd.in/dT84mYzt) and 8 months
in DIB – Dipartimento di Informatica – UniBa.
For further information, please contact prof. Giovanna Castellano:
giovanna.castellano@uniba.it
########################################################
*PhD scholarship for graduates in Computer Science or Mathematics*, on
‘Explainable computational methods for the analysis of bioinformatics data
produced by high-throughput CRISPR screening’, to be carried out at the
Institute of Biomedical Technologies of the CNR and the Mathematical or
Computer Science Department, in Bari (Italy).
Knowledge in programming and data analysis is required, while
bioinformatics skills will be acquired during the PhD.
Call for applications: https://lnkd.in/diDYsV_7 (page 41 ITA version and
page 36 ENG version) ID code CN00000041, CUP H93C22000430007
Deadline for application: December 19, 2022
Please contact arianna.consiglio@cnr.it if interested
Abstract:
CRISPR/Cas9 technology allows cutting a target region on DNA to perform
specific changes to a cell’s genome. The versatility of this technique has
allowed the development of countless applications in Biomedicine and opens
up new scenarios in the treatment of complex pathologies such as genetic
diseases and diseases with multiple DNA recombinations, such as tumors. The
large amount of data produced while testing the efficacy of this technology
requires the definition of innovative analysis methods that provide
explainable predictions regarding the possible effects of genomic
modifications on cellular systems.
The project will include the study of possible genetic modifications which,
induced in pathological cells, can restore their physiological activities.
In particular, aspects such as the feasibility of applying the
modification, the efficiency of its introduction into the cellular model,
the positive and negative effects that the genetic modification can cause
in the system will be predicted and evaluated.
National Institute of Mental Health
The Machine Learning Team at the National Institute of Mental Health (NIMH)
in Bethesda, MD, has an open position for a machine learning research
scientist. The NIMH is the leading federal agency for research on mental
disorders and neuroscience, and part of the National Institutes of Health
(NIH). A copy of this ad may be found at
https://nih-fmrif.github.io/ml/index.html
### About the NIMH Machine Learning Team
Our mission is to help NIMH scientists use machine learning methods to
address a diverse set of research problems in clinical and cognitive
psychology and neuroscience. These range from identifying biomarkers for
aiding diagnoses to creating and testing models of mental processes in
healthy subjects. Our overarching goal is to use machine learning to
improve every aspect of the scientific effort, from helping discover or
develop theories to generating actionable results.
We work with many different data types, e.g. very large brain imaging
datasets in various imaging modalities, neural recordings, behavioral data,
and picture and text corpora. We have excellent computational resources,
both of our own (tens of high-end GPUs for deep learning, several large
servers) and shared within the NIH (a cluster with hundreds of thousands of
CPUs, and hundreds of GPUs).
As a machine learning research group, we develop new methods and publish in
the main machine learning conferences (e.g. NeurIPS and ICLR), as well as
in psychology and neuroscience journals. Many of our problems require
devising research approaches that combine imaging and non-imaging data, and
leveraging structured knowledge resources (databases, scientific
literature, etc) to generate explanations and hypotheses. You can find more
about our work and recent publications at
https://cmn.nimh.nih.gov/mlt
### About the position
This position requires experience in the use of deep learning in the
context of substantial research projects, ideally having led to
publications (or preprints). As our team works on both applications and
method development, here are some examples of projects we have carried out
or are presently engaged in:
– Bayesian deep neural networks for brain segmentation with uncertainty
– convolutional neural networks on structural or functional brain MRI for
decoding information or person characteristics
– a comparison of approaches for generating gradient-based saliency maps
for neural networks in brain imaging data
– a method for distributed training and consolidation of Bayesian deep
neural networks
– modifications of neural network models of vision to test hypotheses about
visual representations in the brain
– transformer models for predicting fine-grained content labels in text
transcripts from therapy sessions
– improving transfer learning in neuroimaging
– fine-tuning of large language models for emulation of participants in
psychology experiments
Please emphasize this aspect of your experience in your application.
In general, we are seeking candidates who are capable of combining machine
learning, statistical, and domain-specific computational tools to solve
practical data analysis challenges (e.g. designing experiments, generating
and testing statistical hypotheses, training and interpreting predictive
models, and developing novel models and methods). Additionally, candidates
should be capable of visualizing and communicating findings to a broad
scientific audience, as well as explaining the details of relevant methods
to researchers in a variety of domains.
Other desirable experience includes:
– mathematical optimization (e.g. convex, linear programming, integer
programming)
– statistical inference (e.g. generalized linear models, mixed effect
models, state space models, survival analysis)
– reinforcement learning
– Bayesian statistical modelling
– other types of modelling of human/animal learning and decision-making
– neuroimaging data processing/ analysis (any MRI modality, MEG, or EEG)
– modelling of other types of neural data (e.g. neural recordings, calcium
imaging)
Finally, you should have demonstrable experience programming in languages
currently used in data-intensive, scientific computing, such as Python,
MATLAB or R. Experience with handling large datasets in high performance
computing settings is also very valuable. Although this position requires a
Ph.D. in a STEM discipline, we will consider applicants from a variety of
backgrounds, as their research experience is the most important factor.
Backgrounds of team members include computer science, statistics,
mathematics, and biomedical engineering.
This is an ideal position for someone who wants to establish a research
career in methods development and applications driven by scientific and
clinical needs. Given our access to a variety of collaborators and large or
unique datasets, there is ample opportunity to match research interests
with novel research problems. We also maintain collaborations outside of
the NIH, driven by our own research interests or community impact.
If you would like to be considered for this position, please send
francisco.pereira@nih.gov a CV, with your email serving as a cover letter.
We especially encourage applications from members of underrepresented
groups in the machine learning research community. If you already have a
research statement, please feel free to send that as well. There is no need
for reference letters at this stage. Other inquiries are also welcome.
Thank you for your attention and interest!
Nanyang Technological University
Our Deep NeuroCognition Lab in NTU and A*STAR, Singapore is currently
recruiting multiple PhD students and postdocs. Research experiences in
neuroscience, cognitive science and AI are preferred.
Students and staff members will receive competitive monthly
salaries/scholarships and other benefits (e.g. medical insurance, annual
leave, sick leave).
Join us: https://a0091624.wixsite.com/deepneurocognition-1/join-us
If you are interested in applying,
send your CV + research statement to Mengmi Zhang (a0091624@gmail.com).
University Hospital of North-Norway
*Data scientist / ML engineer – ML for health, The Norwegian Centre for
Clinical Artificial Intelligence (SPKI), University Hospital of
North-Norway*
SPKI is expanding, and is in search of a highly motivated data scientist /
ML engineer who wants to contribute to the development and implementation
of new artificial intelligence (AI) tools for health.
The work will be done in a highly interdisciplinary environment, and you
will collaborate with a team consisting of clinicians, scientists from the
university and technologists, legal experts, industry partners, as well as
personnel responsible for ICT, data security, privacy concerns and more.
This environment also includes researchers at The Machine Learning Group
and Visual Intelligence .
For details, please see:
https://www.finn.no/job/fulltime/ad.html?finnkode=283131608
University of Glasgow
We are recruiting a PhD student to work on structured generative
models of images and videos, in the School of Computing Science at the
University of Glasgow.
Possible research topics include:
– deep generative models of images representing their latent 3D structure
– structured models for video incorporating physical constraints
– object-centric and compositional generative models
You should have a good degree in computer science / maths / similar,
and excellent coding skills. Funding is available for strong
candidates; there is a deadline for scholarships at the end of
January.
Please contact me directly (via paul.henderson@glasgow.ac.uk) before
applying, to discuss suitability and negotiate a research topic.
Include your CV, transcripts, and a short (one page) research
proposal.
The University of Glasgow (est. 1451) is a member of the UK’s Russell
Group of leading research universities. It is ranked 5th in the U.K.
for computer science [Times Good University Guide 2022]. The School of
Computing Science has over 70 faculty, and over 130 research students.
The School particularly encourages applications from groups who are
traditionally under-represented in computer science.
Best regards,
Dr. Paul Henderson
Assistant Professor (Lecturer)
School of Computing Science
University of Glasgow
https://www.pmh47.net