Openings

This are full-time working positions in our lab, often associated to research projects for which we seek specific expertise. This figure is usually mature, and has research experience. From a scientific perspective, he/she might have either strong experience in biological data analysis, or strong experience in data science and machine learning. Whatever is the case, we offer the necessary complementary training.

For this positions, we can host applicants for national and international postdoc fellowships such as Marie Curie, AIRC etc; in this way, we host postdoctoral scientists that will come to our lab to develop, under our supervision, their own research projects. For these positions you do have to get in touch well before the deadlines if you want to discuss your potential research project hosted in our lab.

Project-based positions

• none at the moment

External calls where we can host applicants (example)

• Marie Curie Actions

• AIRC fellowships

There are many ways of getting to do a PhD with us, in Trieste and in the broader ecosystem of Italian universities, and we are happy to help you find the best option based on your background and interests. Not all potential calls fit to your background, and therefore finding the right one to target is a key aspect of a PhD application.

Formally, the lab PI (Giulio Caravagna) can mentor students enrolled in 3 PhD programs within the Trieste area:

• Applied Data Science and Artificial Intelligence (ADSAI), University of Trieste, Trieste, Italy:

• Theoretical and Scientific Data Science, SISSA, Trieste, Italy:

• National PhD Program in AI, Health and Life Sciences (based in SISSA, Trieste, Italy).

We are always open to students co-supervision, and so this is also an opportunity for us to tight a collaboration with other labs (or to co-supervise students from other labs as well).

Please do get in touch to discuss your interests before you apply for any of the following programs.

Based on projects need or general lab development, we may hire staff scientists that are often bioinformaticians or scientific programmers. These positions can also be offered to freshly graduates that do not feel to pursue a PhD position in the short time. 

Available positions

• none at the moment

The best way to start working in Data Science for genomics is to get your hands dirty with some real project.

We are always interested in supervising talented students that seek to complete their training in any of the Data Science programs offered in the wider Trieste area, or even elsewhere. 

In most cases, to get a placement with us, you should 

• have acquired some working knowledge of Machine Learning (e.g., you can write a model in a probabilistic program, such as those taught in the "Probabilistic Machine Learning" course at the DSSC Trieste program);

• have an idea of what we do in the CDS lab, which can be easily achieved by taking the MSc course(s) we teach at the DSSC Trieste program, or watching some recordings from our classes, or attending our lab meetings (get in touch with us if you want to discover what we do). 

A thesis with us consists usually in developing some novel Machine Learning model and a novel R/Python package, and requires you to work together with other lab members as a team. A direct supervision with the PI is also in place, with regular weekly meetings for project updates.  

So far, thanks to our brilliant students, we always managed to publish every thesis as a research paper. 

Example past DSSC theses

• Rcongas/congas - Copy Number calling from single-cell RNA sequencing

• lineaGT/pylineagt - Bayesian multi-lineage inference from gene therapy assays

• basilica/pybasilica - Bayesian somatic signature learning with a catalogue

Please do get in touch to discuss your potential thesis topic, and feel free to propose your ideas.

Because our Bachelor AI program is new in Trieste, we have not supervised final students yet.

However, it's always a good time to start, even for us. These kind of projects are usually simpler than master ones, and will be attached to something already existing in the lab (e.g., you might help implementing some model and testing it on real data  analysis, always with the supervision of other lab members). 

Requirements for these projects will be discussed on an individual basis.

Do get in touch if interested.