Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
on items directly related to the research activity. Where to apply Website http://www.fapesp.br/oportunidades/9615 Requirements Additional Information Eligibility criteria Eligible destination country/ies
-
involving large companies under the “Autonomous Systems, Robotics, and Machine Tools” track of the IASMIN Platform (a FAPESP Applied Research Center in AI). The work is to be carried out on-site
-
machine learning algorithms to support the traceability of the beef’s geographical origin. She/he will participate in all stages of the project, including planning and supervision of sample collection and
-
computing, obtained within a maximum of 7 years; application of machine learning and deep learning methods to remote sensing images; proficiency in programming (R, Python, or similar); ability to work
-
in tropical regions; analyze links between macrofauna and soil carbon; build/validate scoring algorithms using machine learning/cumulative functions. Outputs – Lead scientific, technical, and policy
-
areas: genome assembly, gene annotation, transposable element annotation, comparative genomics, RNA-seq, phylogenomics, gene family analysis, or functional analyses. Desirable requirements i) Ability
-
particulate processing technologies such as drying, fluidized-bed, high-shear, and steam agglomeration, as well as pilot-scale ingredient development. Experience in physicochemical, techno-functional, and
-
position within a Research Infrastructure? No Offer Description Activities and context: The fellow will develop machine-learning interatomic potentials (MLPs), trained on density functional theory-DFT data
-
chromatography. The candidate will prepare samples for immunopeptidomics by liquid chromatography coupled with mass spectrometry tandem (LC-MS/MS), analyze large datasets, and develop scripts and machine learning
-
; • Familiarity with statistical analyses and modeling as well as machine learning approaches, supported with strong skills in computational optimization of methods; • Experience working with large-scale datasets