Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- University of Oslo
- AbbVie
- Genentech
- University of North Carolina at Charlotte
- Indiana University
- King Abdullah University of Science and Technology
- Nature Careers
- University of Bergen
- Harvard University
- Humboldt-Stiftung Foundation
- Montana State University
- Nanyang Technological University
- Queensland University of Technology
- Simons Foundation
- University of Rhode Island
- University of South-Eastern Norway
- Aarhus University
- Cornell University
- EMBL-EBI - European Bioinformatics Institute
- ETH Zurich
- Emory University
- Florida Atlantic University
- George Mason University
- Georgetown University
- Hiroshima University
- Johns Hopkins University
- NTNU - Norwegian University of Science and Technology
- National Renewable Energy Laboratory NREL
- National University of Singapore
- Northeastern University
- Purdue University
- SUNY University at Buffalo
- SciLifeLab
- The University of Memphis
- University of British Columbia
- University of Michigan
- University of Saskatchewan
- University of Texas at Austin
- 28 more »
- « less
-
Field
-
Kannapolis and UNC Charlotte in Charlotte. Experience in yeast genomics, RNA-sequencing, bioinformatics, or machine learning is preferred. Departmental Preferred Experience, Skills, Training/Education
-
, molecular dynamics, and machine learning, to model battery electrolyte and solid electrolyte interphase (SEI), while collaborating with experimentalists. Qualifications • Ph.D. in Computational Materials
-
apply cutting-edge machine learning algorithms, with focus on foundation models and LLMs/agents, to analyze complex biological data. This data includes gsingle cell genomics profiles, spatial data, and
-
, computational, and machine learning/AI methods, with a particular emphasis on deep learning approaches improve our understanding and prediction of infectious disease dynamics. Projects are also strongly grounded
-
discoveries. Who You Are: Ph.D. with a proven track record of excellence in Computer Science and Machine Learning, with substantial domain experience in biology and genomics. Must have advanced at least one key
-
both the NCRC in Kannapolis and UNC Charlotte in Charlotte. Experience in yeast genomics, RNA-sequencing, bioinformatics, or machine learning is preferred. Departmental Preferred Experience, Skills
-
evaluate machine learning approaches for predicting clinically successful drug targets. For this work, the postdoc will have access to a large high-performance compute cluster and to AbbVie's cutting-edge
-
, single-cell analysis, and machine/deep learning (preferred but not required). Strong programming and statistical skills (e.g., Python, Perl, R, Bash). Track record of first-author research papers. Strong
-
Computer Science, Biomedical Engineering, Pathology Informatics, or a related field, with emphasis on computer vision and machine learning (summer and fall graduates are also welcome to apply) Proficiency in
-
transcriptomic data, that will be integrated with clinical metadata and whole-genome data for developing machine learning models to identify and predict patient factors driving toxicity response and sensitivity