Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Nature Careers
- University of Bergen
- CNRS
- Institute for bioengineering of Catalonia, IBEC
- KINGS COLLEGE LONDON
- Radix Trading LLC
- UNIVERSIDAD POLITECNICA DE MADRID
- UNIVERSITY OF HELSINKI
- University of A Coruña
- Wageningen University and Research Center
- ; University of Cambridge
- Carl von Ossietzky Universität Oldenburg
- Delft University of Technology (TU Delft)
- Delft University of Technology (TU Delft); yesterday published
- Leibniz
- New York University
- SciLifeLab
- The Ohio State University
- The University of Iowa
- Universidad de Alicante
- University of Antwerp
- University of Cambridge
- University of Newcastle
- University of Nottingham
- University of Oxford
- University of Texas at Austin
- 16 more »
- « less
-
Field
-
in AI and machine learning – from classical approaches to large language models. You are proficient in Python and key ML libraries (e.g. scikit-learn, PyTorch, LLM APIs), and you have a track record of
-
Experience in statistical analyses of ecological data Experience with programming in R or Python is an advantage Dependable, pro-active and self-motivated Flexible, adaptive and innovative We offer you
-
• Understanding of agricultural production systems in the U.S • Experience working with spatial data and machine learning models. • Strong knowledge of programming languages, such as Python, R . • Demonstrated
-
documents, including OCR post-processing and parsing of legacy texts Proficiency in scientific programming (preferably Python), version control (e.g. Git), and data standards such as RDF and Darwin Core
-
Python for scripting and data analysis, metabolite ID via MS/MS and annotation (e.g. SIRIUS, HMDB, authentic libraries etc.), statistical uni- and multivariate analysis, data visualization (PCA score
-
different streams. Apply for grant applications that lie in the center’s strategic interest and integrate research and advance the center’s research portfolio. Facilitate Knowledge Integration: Bridge work
-
to the development of deep learning methods to predict reaction outcomes and optimal reaction conditions for organic reactions. The work will involve model development using Python and/or other programming languages
-
the analysis of gene expression and neuronal activity across different models, with the ultimate goal of contributing to the development of treatments that could modify the neurodegenerative progression
-
for sleep apnea detection, monitoring and treatment: a multimodal approach for individuals with different health conditions” (mHealthSleep4U)” This fellowship is associated with the research project
-
: Develop, lead and carry out research activities that cut across multiple research and innovation areas. Contribute to joint research outputs and innovation prototypes that integrate results from different