PostDoc - AI-Powered Interpretation of Missense Variants in Actionable Genes (Closed)

This project aims at the development of clinically useful artificial intelligence-based tools to facilitate the accurate diagnosis and prognosis of diseases related to gene variants (mutations) in actionable genes, with special emphasis on those encoding ion channels.

Ion channels are a class of proteins found in most cells which allow the passage of specific ions through the cell membrane. This is needed for a number of physiological functions such as the working of neurons or the contraction of the heart and of skeletal muscle cells. Inherited and de novo mutations in ion channels have been associated with diseases such as epilepsy, hypertension and schizophrenia. Patients suffering from these diseases often need personalized treatments, which must start with the correct identification of the responsible gene variants. Thus, in the current context of rapid advancements in genetic technologies which allow DNA sequencing to be performed routinely, the problem of correctly discriminating tolerated from pathogenic gene mutations, and assessing a reliable prognosis of the severity of the associated pathology, is a very challenging and important one in clinical practice.

In the terminology of machine learning (the area of artificial intelligence aimed at teaching computers to solve problems without being explicitly programmed) this is known as a classification problem. These are commonly approached as supervised learning problems, in which each example is assigned a list of features together with a label, and then a predictive model is fit to this data set. This model can later be used to infer the label for unknown examples.

For several families of ion channels, there is a reasonably high number of gene variants whose tolerated/pathogenic condition is known. This would allow the development of sufficiently accurate supervised learning models to be useful in a clinical setting. For example, our group has recently developed one such model (Mle-KCNQ2) specific for the epilepsy-related Kv7.2 potassium ion channel (accessible at https://channels.bcb.eus), which manages to classify both tolerated and pathogenic variants better than the best previously available tools. The general idea of this project is to apply the kind of methods used in this Kv7.2 ion channel variant classifier to other disease-associated actionable channels.

- IA: Knowledge of Machine Learning and Deep Learning

- Python programming

- Degree in Chemistry, Physics, Mathematics or similar

• Estimated annual gross salary: Salary is commensurate with qualifications and consistent with our pay scales
• Target start date: 2024/09/15

We provide a highly stimulating research environment, and unique professional career development opportunities.

We offer and promote a diverse and inclusive environment and welcomes applicants regardless of age, disability, gender, nationality, ethnicity, religion, sexual orientation or gender identity.

DIPC is a research center whose mission is to perform and catalyze cutting-edge research in physics and related disciplines, as well as to convey scientific culture to society. Located in Donostia / San Sebastian (Basque Country, Spain), DIPC was born from a strategic alliance of both public institutions and private companies. Since 2008 DIPC is a 'Basque Excellence Research Center' (BERC) recognized by the Basque Government's Department of Education. In 2019, DIPC was also recognized as a "Severo Ochoa" Excellence Center by the Spanish Research Agency.

We are a multidisciplinary group made up of: physicists, chemists, biochemists and mathematicians who have recently come together to address biophysics problems. Our mission is to offer complementary computational support to any laboratory or industry in the BIO world that wishes to integrate in-silico techniques into experiments.

Furhter information about the group can be found here: https://bcb.eus/

Interested candidates should submit an updated CV and a brief statement of interest to the following application email below. 

Reference letters are welcome but not indispensable.

The reference of the specific opening to which the candidate is applying should be stated in the subject line, and the application must be received before the application deadline.

Although candidates are welcome to contact the project supervisors to know further details about the proposed research activity, please be aware that the application will be evaluated only if it is submitted directly to the email address listed below as application email.

• Reference: 2024/22
• Application deadline: 2024/07/21
• Application email: jobs.research@dipc.org

Applications received by the deadline will be evaluated by a Committee designed by the DIPC board on the basis of the following criteria:

• CV of the candidate (40%)
• Adequacy of the candidate’s scientific background to the project (40%)
• Reference letters (10%)
• Other: Diversity in gender, race, nationality, etc. (10%)

Evaluation results will be communicated to the candidates soon after. Positions will only be filled if qualified candidates are found.

The DIPC may revoke its decision if the candidate fails to join by the appointed time, in which case the position will be awarded to the candidate with the next highest score, provided it is above 50 (out of 100).

However, the selected candidate may keep the position if, in the opinion of the Selection Committee, the candidate duly justifies the reasons why he or she cannot join before the specified deadline, and as long as the project allows it.