Tenure Track position for a Researcher in Artificial Intelligence models for Precision Medicine

 

The Bruno Kessler Foundation (FBK) conducts research activities in Information Technology, Materials and Microsystems, Theoretical Physics, Mathematics, Italian-Germanic historical studies, Religious studies and International Relations. Through its network, it also develops research in the fields of international relationships, conflict causes and effects, European economic institutions, behavioral economics and evaluative assessment of public policies.

 

The  Digital Health & Wellbeing Center is one of the Centers of the Bruno Kessler Foundation (FBK). The activities of the Center for Digital Health & Wellbeing mainly concern scientific research of excellence in the field of Computer Science and AI techniques and methodologies for health and healthcare, as well as social and technological innovation for a relevant impact on both the local community and nationally and internationally.

 

The Data Science for Health (DSH) Research Unit focuses on the design, the development and the implementation of predictive models for the life sciences. In particular, statistical machine learning and deep learning algorithms are applied to integrated health data such as Electronic Health Records (EHR), different levels of -omics data and bioimages of diverse nature, from CT, PET, MRI scan to Digital Pathology WSIs. The aim is to create novel mathematical methods and ICT platforms connecting physiopathological patterns of disease with high dimensional data now available for Functional Genomics (e.g DNA microarrays, SNPs, proteomics, Deep Sequencing), with clinical and imaging data and geodatabases of environmental factors and socio-demographic data.

FBK actively seeks diversity and inclusion in the workplace and is also committed to promoting gender equality.  

 

Job Description

The candidate will use statistical machine learning algorithms, both shallow and deep, to develop, optimize, implement, apply, and validate predictive models for the life science,  – in particular in the health domain. Training data will consist of EHRs, PGHD, different levels of omics data, biomedical images and digital pathology WSIs provided by the collaborating labs. Particular attention will be devoted to the reproducibility of the methodology, the interpretability of the algorithm(s), the data integration and the translatability of the models into clinical support tools.

Main Tasks

  • Identify and discuss with the partners the diagnostic/prognostic tasks.
  • Organize, clean, and prepare the provided data.
  • Follow all the steps of a learning pipeline using the available data to tackle the  task of interest.
  • Implement and validate the solution on a computational infrastructure.
  • Discuss the solution with the collaborating partners.
  • Disseminate the findings on scientific paper and talks.
  • Help supervising B.Sc, M.Sc. and Ph.D. students.
  • Help writing grants and projects.
  • Help managing research projects. 
 

The Ideal Candidate shall have: 

  • PhD Degree in Biomolecular Sciences, Mathematics, or related fields  
  • Research experience in the field of Statistics, Machine Learning, both supervised and unsupervised, deep learning and Mathematical Modeling.
  • Solid knowledge in bioimages processing and experience in medical data preprocessing and cleaning.
  • Research experience in model implementation in Python, excellent knowledge of the PyTorch interface for deep learning and basic knowledge of the Tensorflow and Keras interfaces
  • Established publication record in international peer-reviewed journals and established record of poster/oral presentations in international conferences.Time management, planning, and development skills. Accuracy, flexibility, proactivity, and goal orientation.
  • Teamwork approach, good communication and relational skills.
 

Additional skills

  • Ability to build  graphical interfaces and knowledge of the basics of data visualization.
  • Knowledge of model implementation on HPC solutions and basic knowledge of the R language.       
 


Employment

Type of contract: Tenure Track position leading to an FBK 3rd Level Researcher permanent contract. The maximum duration of the Tenure Track contract is 5 years (which may be reduced depending on the assessment by the Committee of the candidate's abilities and skills). If the final assessment of the Tenure Track experience is positive, the selected candidate for the tenure track position will be offered a permanent contract.

Start date: February 2023

Working hours: full time.

Gross annual salary: about 39.300 €, plus objectives achievement bonus.

Benefits: flexi-time, company subsidized cafeteria or meal vouchers, internal car park, welcome office support for visa formalities, accommodation, social security (SANIFONDS), family-work balance, free training courses, support on bank account opening, discount on public transport, sport, language course fees. More info at  https://www.fbk.eu/en/work-with-us/  

Workplace: Povo, Trento (Italy)

Application

Interested candidates are requested to submit their application by completing the online form (https://hr.fbk.eu/en/jobs). Please make sure that your application contains the following attachments (in pdf format):

  • detailed CV;
  • motivational letter indicating why the candidate is suitable for this position.
 

Application deadline: 12 December 2022

 

Please read our Regulations “Guidelines for Tenure Track positions” and Tenure Track FAQ before completing your application.

For further information, please contact the Human Resources Services at jobs@fbk.eu




 
Business units
Centro Digital Health& Wellbeing
Locations
Humanities and Social Sciences Hub - Trento
Cookie policy
x
We use technical cookies, that are always enabled and necessary for the website to work correctly, and analytic and profiling cookies, including third party ones, to allow us to measure the usage and performance of the web site and send advertising, including targeted advertising. To accept all cookies, click «Accept». To manage or disable cookies click on «Manage». To refuse all cookies and close the banner click on «x»; in this case you can continue to navigate the site and only technical cookies will be used. If you would like to learn more, please read our Cookie Policy