Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- University of Oslo
- Harvard University
- Nanyang Technological University
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- University of British Columbia
- CRANFIELD UNIVERSITY
- NTNU - Norwegian University of Science and Technology
- National University of Singapore
- The University of Queensland
- UiT The Arctic University of Norway
- University of Michigan
- University of Texas at Austin
- Dana-Farber Cancer Institute (DFCI)
- Johns Hopkins University
- Monash University
- University of Agder
- University of Algarve
- University of Birmingham
- University of New South Wales
- University of South-Eastern Norway
- University of Stavanger
- Western Norway University of Applied Sciences
- ADELAIDE UNIVERSITY
- Aarhus University
- Carnegie Mellon University
- Central Michigan University
- City of Hope
- Cori Institute of Molecular and Computational Metabolism
- Cornell University
- Cranfield University
- ETH Zürich
- Ellison Institute of Technology
- Francis Crick Institute
- Hong Kong Polytechnic University
- Humboldt-Universität zu Berlin
- INESC ID
- INESC TEC
- Indiana University
- King's College London
- Marquette University
- Max-Planck-Institut für Bildungsforschung
- National Research Council Canada
- Northeastern University
- Oden Institute for Computational Engineering and Sciences
- Research Center for Molecular Medicine (CeMM), ÖAW
- The University of Manchester;
- UCL;
- University of Bergen
- University of Cambridge;
- University of Denver
- University of Exeter;
- University of Glasgow
- University of Hertfordshire;
- University of Idaho
- University of Manchester
- University of Michigan (U-M)
- University of Nottingham
- University of Nottingham;
- University of Otago
- University of Sheffield
- University of Sydney
- University of Tübingen
- Zintellect
- 53 more »
- « less
-
Field
-
Oden Institute for Computational Engineering and Sciences | Austin, Texas | United States | 2 months ago
genetic data Multimodal machine learning for biological discovery Translational genomics and risk modeling The fellow will work in an environment that emphasizes methodological innovation, statistical rigor
-
research questions. Strong quantitative research skills and proficiency in Python or R. Experience with large-scale textual data, natural language processing, machine learning, transformer-based models
-
Experience in one or more of the following areas is preferred: Statistical genetics Human genetics Population genetics Evolutionary genetics Bayesian statistics Machine learning Large-scale genomic data
-
Research Center for Molecular Medicine (CeMM), ÖAW | Graz 12 Bez Andritz, Steiermark | Austria | 2 months ago
; and how these mechanisms can be understood, modelled and ultimately perturbed for biomedical discovery. Two scientific tracks Track 1: Computational Biology / Machine Learning for membrane protein
-
reactions. Experience applying Machine Learning to optimise and guide iterative laboratory experiments. Experience of oligonucleotide design and of adapting an amplification method to new target sequences
-
will be preferred. Requirements: PhD degree in learning sciences, educational technology, human-computer interaction (HCI), information technology, AI or relevant fields Prior experience and proficiency
-
: • Statistical programming and data analysis using R, Python, Stata, or similar tools • Experience with machine learning or AI methods for health data analysis • Experience working with large health datasets
-
assigned by Supervisor. Requirements PhD in Computer Science or related field Expertise in computer vision and vision-language models Experience with ML evaluation metrics and benchmarking Proficiency in
-
, protein structure modelling, AlphaFold/multimer-based analyses, statistics, data visualization, and interdisciplinary work at the interface of proteomics, structures and machine learning. Track 2
-
to analyze large multi-modal datasets and/or who wish to deploy the next generation of exposome AI models. These positions come with data ready to analyze: the candidate can focus on developing research