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Konrad Zuse School of Excellence in Reliable AI

The *Konrad Zuse School of Excellence in Reliable AI (relAI)*
<https://zuseschoolrelai.de/> aims to train future generations of
artificial intelligence (AI) experts, who for the first time combine
technical brilliance with awareness of the importance of AI’s reliability.

 
*Applications for the relAI PhD program are now open*
 
The current technological revolution is largely driven by spectacular
progress in AI. Yet, although the huge potential is widely recognized, the
lack of a reliable AI technology whose fundamental bases are safety,
security, privacy and responsibility, is still considered a serious issue
of concern, limiting its adoption both by industry and society at large.
 
The relAI program focuses on the mathematical and algorithmic foundations
of reliable AI along with domain knowledge in three core application areas
for which reliable AI methods are most urgently needed: medicine &
healthcare, robotics & interacting systems, and algorithmic decision-making,
 
The relAI school is embedded in the unique transdisciplinary Munich AI
ecosystem, combining the expertise of the two Universities of Excellence*
Technical University of Munich (TUM) and Ludwig Maximilians University of
Munich (LMU)* and closely integrates various AI Centers of leading
universities worldwide and industry partners.
 
*What the relAI PhD program offers*
 
The novel, innovative PhD relAI program offers a cross-sectional training
for successful education in AI including scientific knowledge, professional
development courses and industrial exposure, providing a coherent, yet
flexible and personalised training.
 
Funded applicants will be hired for three years, including social benefits
(TV-L E13 of the German public sector). They are further supported by
travel grants, e.g. for conference attendance or research stays. Doctoral
students enrol at TUM or LMU depending on the hosting relAI fellow.
<https://zuseschoolrelai.de/fellows-overview/>
 
*Eligibility*
 
Excellent master’s degree (or equivalent)* in computer science,
mathematics, engineering, natural sciences or other data science/machine
learning/AI related disciplines.
 
Applicants should have a genuine interest to work on a topic of reliable AI
covering aspects such as safety, security, privacy and responsibility in
one relAI’s research areas Mathematical & Algorithmic foundations,
Algorithmic Decision-Making, Medicine & Healthcare or Robotics &
Interacting Systems.
 
*How to apply*
 
Applications are sent online via this website
<https://zuseschoolrelai.de/application/>. We refer interested candidates
to the relAI website <https://zuseschoolrelai.de/application/> for more
information about the call requirements.
 
Deadline for applications is *January 9th, 2023*.
 
*If you are still studying for your master’s degree you may send a bona
fide statement/transcript from the university, stating the examination
marks already obtained. In addition, you will have to finish your master’s
studies before starting the PhD.

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University of Texas

*Position Information:* *immediate fill of fully funded Ph.D. positions for
Fall/Spring 2023* at the Center on Stochastic Modeling, Optimization, &
Statistics (COSMOS) at the University of Texas at Arlington. Graduate
students with *strong quantitative background and programming skills* are
encouraged to apply. The research directions include *Advanced* *Analytics,
Deep Learning*, *Machine Learning, *and *AI-driven Intelligent UAV-UGV
Systems. *
 
If you are interested, please send your CV to *Dr. Shouyi Wang at *
*shouyiw@uta.edu* <shouyiw@uta.edu>. We will evaluate your CV and give
feedback quickly. *Accepted Ph.D. students will work with Dr. Wang and will
be provided** with full financial support for the entire Ph.D. study*. *Visiting
Students/Scholars Positions *are also available in Deep Learning and
Intelligent Systems.
 
*Lab** Information: *The COSMOS Center is conducting world-leading research
in Advanced Data Analytics, Machine Learning Research, Complex System
Optimization & Modeling, Healthcare, and Biomedical Informatics, and
AI-driven Technologies. The lab has very strong connections to the AI
industry. The renowned research collaborators include *American Airlines,
Apple, Foxconn, IBM, Facebook/Oculus, Samsung/Harman AI Group, and NVIDIA.*
 
*The COSMOS Center Ph.D. graduates are highly competitive in the US job
market.* Recent Ph.D. graduates of COSMOS Center obtained jobs at *American
Airlines,** United Airlines, UPS, CSX, AT&T, Alibaba/Seattle, Bank of
America, BNSF, General Motors, etc.* *Our Ph.D. graduates also have immense
opportunities to pursue academic careers. *Recent Ph.D. students obtained
faculty positions at top schools, including the *University of Illinois
Chicago, Washington State University, Stevens Institute of Technology,
University of Southern California, The State University of New York at
Buffalo, Harbin Institute of Technology, etc*.
*The Current Research Projects & Topics include:*
* Deep Learning for drug discovery (e.g., protein, DNA/RNA, molecule deep
learning in life science, sponsored by NSF & NIH)
* Deep Learning for medical imaging analytics (work with the University of
Washington)
* AI-Driven UAV-UGV Collaborative Smart Systems for Agriculture
Applications (work with USDA)
* Probabilistic Deep Learning and Stochastic Optimization for Complex
Systems Decision Analytics with Applications to Energy Systems and Smart
Grids Operations Management (NSF)
* Interpretable Deep Learning/Machine Learning Research for Biomedical Data
Mining & Knowledge Discovery
* Deep Learning for Satellite Imaging Analytics and Geographic Information
Systems (USDA)
 
*School Information:* With more than *50,000* enrolled students, UTA is one
of the largest public universities in the nation. According to the 2022
U.S. News and World Report, *UTA Engineering School is ranked #79 in the US
<https://www.usnews.com/best-graduate-schools/top-engineering-schools/university-of-texas-arlington-02177#:~:text=The%20University%20of%20Texas%20at%20Arlington%20is%20ranked%20No.,widely%20accepted%20indicators%20of%20excellence.&text=See%20how%20this%20school%20scored,indicators%20used%20in%20the%20rankings.>*.
The Industrial, Manufacturing, & Systems Engineering (IMSE) Department is
ranked *#50* for Graduate Programs
<https://www.uta.edu/uta/about/rankings.php>. With great resources and
Texas State support, the IMSE department is one of the fastest-growing
programs in the US with more than 500 graduate students. UTA is one of the
146 Carnegie R-1 Doctoral Universities in the US with the highest research
activities.
<https://en.wikipedia.org/wiki/List_of_research_universities_in_the_United_States#Universities_classified_as_%22R1:_Doctoral_Universities_%E2%80%93_Very_high_research_activity%22>
*DFW/Arlington Area:* Located in the US’s 4th largest metropolitan area
(Dallas/Fort Worth Area), the COSMOS Center at UTA is conducting
world-leading research projects and has strong industry support and
collaborations.

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Uppsala University

Open PhD position: a fully funded PhD position towards Bayesian
computational methods in Epidemics, specifically using non-traditional
sources of data. The tentative project title is simply “Computational
Epidemics”; more information about this project can be found here:
https://user.it.uu.se/~stefane/formalia/TDB2022_SLL-eSSENCE-PhD.pdf
 
Apply via the online system:
https://www.uu.se/en/about-uu/join-us/details/?positionId=555466
 
Earliest starting date: January 2023 (and as agreed). Deadline to apply
December 16th!
 
This position is placed at the newly created eSSENCE-SciLifeLab graduate
school in data-intensive science and thus comes equipped with travel
funding, newly created PhD courses, and an overall dynamic environment. A
bit more information can be found here:
https://essenceofescience.se/graduate-school/

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University of Bremen

The Computational Neuroscience group at the University of Bremen headed by
Prof. Dr. Klaus Pawelzik offers – under the condition of job release – at
the earliest possible date a position until February 28, 2025
 
 
*Research Assistant /Postdoc in Computational Neuroscience (f/m/d)*
 
German federal pay scale E13 TV-L (100 %)
 
for the research project
 
*Efficient Implementation of Spike-by-Spike Neural Networks using
Stochastic and ApproximativeTechniques*
 
 
The overarching goal of our project is to improve the efficiency of spiking
artificial neural networks using hardware and algorithmic approximation
techniques. Specifically, the project focuses on Spike-by-Spike networks
since they offer a balance between computational requirements and
biological realism which keeps the advantages of the biological networks
while enabling a compact technical realization. To fully take advantage of
the unique features of SbS in terms of robustness and sparseness, dedicated
hardware architectures are required.
 
You would join in with numerical simulations, theoretical analyses, as well
as through the development of new ideas and approaches for boosting the
performance and capabilities of the Spike-By-Spike model. Furthermore, you
would also work on combining Spike-By-Spike networks with non spiking deep
neuronal networks into hybrid models.
 
Details can be found at
https://www.neuro.uni-bremen.de/drupal/open-position-sbs
 
 
*Requirements:*
 
Ideal candidates have a master’s or doctorate degree in Computational
Neurosciences, Physics, Computer Sciences or in related fields. They must
have a strong background in mathematics, programming (preferably in
Python/Matlab), and machine learning as well as an intense interest in
neuroscience. An optimal candidate should not be afraid of cooperating with
engineers, since this is a joint project with a focus on hardware
development. Above all, he/she must have a strong motivation, a sense for
responsibility, a distinct desire to learn, and the ability to proactively
collaborate in an international research environment. Fluency in English is
required (both written and spoken).
 
The Theoretical Neurophysics Lab offers a good working atmosphere, direct
involvement in international research and attractive facilities.
 
The university is family-friendly, diverse and sees itself as an
international university. We therefore welcome all applicants regardless of
gender, nationality, ethnic and social origin, religion/belief, disability,
age, sexual orientation and identity.
 
As the University of Bremen intends to increase the proportion of female
employees in science, women are particularly encouraged to apply. In case
of equal personal aptitudes and qualification priority will be given to
disabled persons.
 
Please explicitly address each of the specified requirements in the
application. Detailed instructions are described below. Please send your
application documents (motivation letter, CV, copy of your degree
certificates including high school, list of skills, awards, publications,
contact details of two academic reference) until December 9th, 2022 by
indicating the job id A326/22 to:
 
University of Bremen FB1
Theoretical Neurophysics lab
Secretary of Prof. Dr. Klaus Pawelzik
Mrs Agnes Janßen
Hochschulring 18, Cognium
D-28359 Bremen
Tel.: +49 421 218 62000
 
in electronic form in a summarized PDF file via e-mail:
ajanssen@neuro.uni-bremen.de.
 
 
For questions of the research project please contact:
 
Prof. Dr. Klaus Pawelzik, E-Mail: pawelzik@neuro.uni-bremen.de
 
 
See also for general information on this topic:
 
http://www.neuro.uni-bremen.de/content/open-position-sbs
 
 
*Detailed instructions for applicants*
 
Your application must comprise:
 
 
*Motivation letter*
 
Your 1-2 page essay should reply the following questions:
 
• What is your background? In which fields have you worked before and how
do you think this can be useful for the present job?
 
• What attracts you to the field of computational neuroscience?
 
• Which problem(s) in computational neuroscience are you most interested in?
 
• What is your motivation to join our project?
 
• What are your plans for your future career?
 
 
*Curriculum Vitae*
 
Send a classical tabular CV with your contact details, and all stages of
education and employment.
 
 
*List of skills, awards, publications*
 
List your skills, especially proficiency in languages (including the level
of proficiency), that you think might be useful for the job. Also list
awards you might have got and peer-reviewed papers, in case there are some.
 
 
*Contact details of two academic references*
 
One of the references should be your MSc advisor. Please contact the
references prior to listing their names so that they are not surprised if
they get contacted.
 
Your application can be in English or German, whatever language you are
more familiar with.

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BCAM

Basque Center for Applied Mathematics is offering a PhD position in
Developing machine learning for polymer systems in the framework of CINEMA
project to work with Jose A. Lozano. The aim of the researcher will be to
develop regression and classification machine learning methods for systems
involving missing/noisy data.
 
Contract: 36 moths
 
Applications: https://cinema-dn.eu/application/
 
Requirements:
 
· MSCA-recruiting rules are applied: not having resided in Spain for
more than 12 months in the 3 years immediately before the recruitment date,
and not having carried out their main activity (work, studies, etc.) in
Spain during this period.
 
· Having a master degree or equivalent diploma, and not having a
doctoral degree.
 
· Background in polymer materials is welcomed
 
· Excellent command of written and spoken English is a must
 
Skills: Ability for research management, dissemination, communication with
colleagues and supervisors, strong teamwork spirit, creativity and problem
solving.
 
Experience: Research experience in the academic or industrial sector will
be considered
 
More info at:
http://www.bcamath.org/en/research/job/ic2022-11-phd-on-developing-machine-learning-for-polymer-systems-cinema