European Medicines Agency
Trainee (Natural Language Processing in Information Processing and Analytics)
Job Description
Selection procedure reference: EMA/TR/10449
Deadline for applications: 20 May 2024 23:59 CET
The European Medicines Agency (EMA) is a decentralised agency of the European Union (EU), located in Amsterdam. It began operating in 1995. It is responsible for the scientific evaluation, supervision and safety monitoring of medicines in the EU.
The 2024 Traineeship programme is now open for applications until 20 May 2024 23:59 CET, with an intake on 1 October 2024.
About the traineeship programme
The Agency is looking for motivated, qualified recent graduates or students on an Erasmus+ or other programme or PhD students or Master’s students (who posses a previous degree), who are interested in gaining experience and contribute to the Agency’s work for every patient in Europe. The purpose of a traineeship at the Agency is to perform tasks that are predominantly in the interest of the trainee’s training and principally serve to increase the trainee’s knowledge and to gain relevant experience.
Placement description
We are looking for a trainee in the Information Processing and Analytics service within the Information Management Division.
Specific objectives and projects
During your traineeship, you will be involved in a project enabling access to unstructured scientific data through natural language processing.
EMA receives a large amount of data around the safety and efficacy of new and/or existing medicinal products, which is used to drive decision making on scientific advice and marketing authorization of medicines. As the amount of available data increases, it becomes increasingly difficult for humans to have a comprehensive overview of all the information and the patterns hidden therein. For this reason, there is an urgent need for algorithms that would enable an easy access to the wanted information (indexing) and that would provide a comprehensive overview of summary statistics on a given topic (reporting).
In this context, you will be involved in looking into ways of extracting relevant scientific information from unstructured/semi-structured documents. In this project, you, together with your mentor and the team, will explore and validate the use of AI-enabled knowledge mining methods for scientific regulatory use cases. To achieve this, a python code will be developed and a common machine learning libraries will be used (e.g. Pandas, Scikit Learn, …) as well as Large Language Models (e.g. Azure OpenAI, Amazon Bedrock, …) and visualisation tools (Streamlit, Shiny, PowerBI, …). The work of this project will be aligned with our cloud architecture principles, leveraging the advantages of cloud native and serverless applications on Azure and/or Amazon Web Services.
Learning outcomes
As a trainee in the Information Processing and Analytics service, you will have the chance to work on real-world analytics and make an impact on defining novel computational methods to be adopted at EMA.
Additionally, you will be able to develop your technical skills, with access to some of the most advanced NLP frameworks and to cutting edge cloud analytics technology.
This is an opportunity to share your academic knowledge and your perspectives related to data science and data analytics whilst getting to know how the Agency works and discover the challenges of the pharmaceutical regulatory lifecycle.
Eligibility criteria
To be eligible for consideration for this placement, you are required to:
- enjoy full rights as a citizen of a European Union Member State or Iceland, Lichtenstein and Norway;
- possess a university degree in Computer Science / Information Management, Physics, Mathematics, Engineering or Life sciences (minimum of three years or more) that must have been obtained within the last 12 months (graduated between 20 May 2023 and 20 May 2024) or be a university student on an Erasmus + or a similar programme or a PhD student or a Master’s student with a previous fully finished degree in the areas mentioned before.
- a thorough knowledge of English (at least level C1) and good knowledge of other official EU language (at least B2) of the Common European Framework for Languages
For criteria 1 and 2, you will be required to provide proof in the application form and at the interview stage. Failing to present these documents may result in the disqualification from the procedure.
Additional skills
In addition to the eligibility criteria, you will have:
- Proficiency in python (or equivalent programming language)
- Analytical mindset
- Affinity with scientific concepts
- Ability to work independently
- Problem solving skills
- Communication skills
Behavioural Competencies
You will demonstrate the following behavioural competencies:
- Communication skills
- Interpersonal skills
- Working with others
- Adaptability
- Research and analytical skills
- Learning and development
Expected selection timelines
Deadline for applications
20 May 2024 23:59 CET
Assessments (remote)
From beginning of June 2024 to beginning of July 2024
Decision and offers
By end of July 2024
Placement start
1 October 2024
Conditions of traineeship
The traineeship is offered for 10 months (1 October 2024 – 31 July 2025) and takes place at the Agency’s premises in Amsterdam with possibility for teleworking up to 40% of working time from The Netherlands and occasional teleworking from outside The Netherlands. Traineeships are offered for either full-time or part-time (80% or 50%) if combined with university studies.
The Agency pays a monthly stipend of €1,858.31 for a full-time traineeship (reduced accordingly for 80% or 50%) and a travel contribution upon joining the Agency.
Each trainee will have a mentor at the Agency who will guide the trainee through the programme.
The conditions of employment are stated in the Executive Decision on rules governing the traineeship programme at the EMA available here.
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Domenico Scarlattilaan 6 – 1083 HS Amsterdam – The Netherlands
Telephone +31 (0)88 781 6000 – Email [email protected]
© European Medicines Agency, 2024. Reproduction is authorised provided the source is acknowledged.
