Sort by
Refine Your Search
-
Category
-
Employer
- IDAEA-CSIC
- CNAG
- Centre Internacional de Mètodes Numèrics en la Enginyeria
- Centro de Investigaciones sobre Desertificación (CIDE, CSIC-UV-GVA)
- Fundacio de Recerca Clinic Barcelona-Institut d'Investigacions Biomediques August Pi i Sunyer
- IFIC
- IMDEA Networks Institute
- UNIVERSIDAD POLITECNICA DE MADRID
- UNIVERSITAT POMPEU FABRA
- Universidade de Vigo
- Universitat Politècnica de Catalunya (UPC)- BarcelonaTECH
- Universitat de Barcelona
- University of Cantabria
- University of Girona (UdG) - Institute of Computational Chemistry and Catalysis (IQCC)
- 4 more »
- « less
-
Field
-
realistic oxide nanoparticles with sizes from a few 10s of atoms to several 1000s of atoms. In each case, machine learned interatomic potentials (MLIPs) will be developed to describe their structure and
-
on machine learning and networks, release the code in open access and actively participate in the international interpretability community. Where to apply Website https://seuelectronica.upc.edu/en/procedures
-
Fundacio de Recerca Clinic Barcelona-Institut d'Investigacions Biomediques August Pi i Sunyer | Spain | 7 days ago
or ELISA. Scientific curiosity, eagerness to learn, and strong teamwork skills will be highly valued. Research group: Cellular Immunotherapies for cancer, Sònia Guedan Project: Next-Generation CAR-T Cells
-
machine learning for audio and acoustics - Research in experimental techniques in acoustic -- - Investigación en procesamiento de señales y aprendizaje automático aplicados al audio y la acústica
-
) Item 03: Proven experience in both programming (Python, R, etc.) and the use of data analysis tools, as well as artificial intelligence / machine learning techniques (punctuation: 22.5) Item 04
-
Position Description The project will focus on the development and application of advanced data-analysis techniques for gravitational-wave science, including machine learning and deep learning
-
advanced instrumentation (ACSM, Xact, SMPS…) and machine-learning techniques. The selected candidate will work in a multidisciplinary and international research environment, contributing to the assessment
-
processes. • Apply machine learning techniques and advanced statistical analysis to extract knowledge from complex datasets. • Participate in the evaluation and optimisation of high-performance scientific
-
Python programming. ● Experience in monitoring code performance. ● 3 or more years of demonstrable experience in machine learning theory. ● Excellent communication and teamwork skills. ● Proficiency in
-
ReDiLEEP training in response diversity methods, data management, reproducible code, R/Tidyverse, machine learning and AI for ecologists, visualisation, evidence-based policymaking, science-policy