Sort by
Refine Your Search
-
Listed
-
Employer
- CIC energiGUNE
- Fundació Hospital Universitari Vall d'Hebron- Institut de recerca
- INSTITUTO DE ASTROFISICA DE CANARIAS (IAC) RESEARCH DIVISION
- Institut de Físiques d'Altes Energies (IFAE)
- UNIVERSIDAD POLITECNICA DE MADRID
- Universitat Politècnica de Catalunya (UPC)- BarcelonaTECH
- Universitat de Barcelona
- Barcelona Supercomputing Center (BSC)
- Computer Vision Center
- ICN2
- IRTA
- Institute for bioengineering of Catalonia, IBEC
- Institute of Photonic Sciences
- Instituto de Neurociencias de Alicante, CSIC-UMH
- Universitat Pompeu Fabra - Department / School of Engineering
- 5 more »
- « less
-
Field
-
environments, specifically Computer Vision, Machine learning algorithms and methods for rock characterization, fragmentation prediction, and mining optimization. Specific Requirements Good academic and
-
, transcriptomics, proteomics), machine learning, statistical analysis and programming languages such as R or Python. - Experience in image analysis, including development of custom ImageJ plugins and workflows
-
atmospheres and detectability studies Model development of 3D stellar atmospheres Applications of machine learning and AI to exoplanet data analysis Biomarkers and habitability of Earth-like planets Where
-
internal reports and manuscripts. Requirements: PhD in Physics, Materials Science, Computational Science/Engineering, Computer Science, or related. Solid knowledge of machine learning, including graph neural
-
pathways, including deactivation processes. Screening and fine-tuning catalysts to enhance performance. Developing workflows and machine learning algorithms to accelerate catalyst design (optional). Group
-
, required to adequately incorporate molecular data, and model regulations of inflammatory and degenerative processes. Available datasets at the molecular level will be incorporated through machine learning
-
, computer science, bioengineering, data science, or a closely related discipline. • Demonstrate advanced proficiency in artificial intelligence and machine learning, particularly in applications involving
-
. Developing workflows and machine learning algorithms to accelerate catalyst design (optional). Group: Atomistic & Molecular Modelling for Catalysis Group Requirements Specific Requirements PhD in Chemistry
-
transcriptomics data analysis. Experience in quantitative image analysis, computer vision, or digital pathology. A strong background in cancer biology or immunology. Experience with machine learning, deep learning
-
Leonardo. The successful candidate will play a crucial role in developing and optimizing machine learning workflows for large-scale environmental data analysis, contributing to the creation of robust and