Project

Advanced machine learning for Innovative Drug Discovery (AIDD)

Goal: The European Marie Skłodowska-Curie Innovative Training Network Advanced machine learning for Innovative Drug Discovery (AIDD) brings together 11 academic institutions and 4 international companies and is supported by 12 partner organisations.

An application of Artificial Intelligence in drug development is not straightforward and requires excellent knowledge of chemistry and biology. The main goal of the AIDD project is to train and prepare a new generation of scientists who have skills both in machine learning and in chemistry and can advance medicinal chemistry and speed up the drug development process.

The project offers 15 Ph.D. positions. Fellows employed will be supervised by academics who have excellent complementary expertise and contributed some of the fundamental AI algorithms which are used billions of times per day in the world, and leading EU Pharma companies who are in charge of new medicine and public health. All developed methods can be used individually but will also contribute to an integrated "One Chemistry" model that can predict outcomes ranging from different properties to molecule generation and synthesis. The network will offer comprehensive, structured training through a well-elaborated Curriculum, online courses, and six Schools.

Every fellow will work 1.5 years in an academic institution and 1.5 years in a company.

For further information see the site of the Project: http://ai-dd.eu

Date: 1 January 2021 - 31 December 2024

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Project log

Pavel V. Karpov
added an update
Samuel Genheden gave a talk on a AIDD seminar about AiZynthFinder, the tool that can find a possible synthetic pathways for any molecule starting from purchasable compounds.
The talk is available here: https://youtu.be/r9Dsxm-mcgA
This rising technology is shaping our future.
 
Pavel V. Karpov
added an update
The project AIDD offers 15 PhD positions (see http://ai-dd.eu/esr-positions for more details).
Requirements for a candidate are as follows:
  1. Master's degree in computer science, physics, chemistry, biology, or engineering with and sincere interest in biology and the life sciences;
  2. prior expertise in one or more of the following fields: machine learning, modeling and simulation;
  3. be excellent in oral and written English with good presentation skills;
  4. possess strong interpersonal skills, excellent written and verbal communication, and the ability to work effectively both independently and in cross-functional teams;
  5. be a highly creative person with outstanding problem-solving ability and the willingness to undertake challenging analysis tasks in a timely fashion.
Additionally,
  1. Excellent software engineering skills are essential. Programming skills in Python must be top-notch;
  2. experience with relevant libraries (TensorFlow/PyTorch, the python scientific stack) is necessary;
  3. good command of modern software development tools, from git to continuous integration pipelines, is an additional plus.
Benefits:
Marie Skłodowska-Curie funding offers highly competitive and attractive salaries. Gross and net amounts are subject to country-specific deductions as well as individual factors such as family allowance.
Eligibility criteria:
  • Early-Stage Researchers (ESRs) shall, at the time of recruitment by the host organization, be in the first four years(full-time equivalent research experience) of their research careers and have not been awarded a doctoral degree;
  • At the time of recruitment by the host organization, researchers must not have resided or carried out their main activity (work, studies, etc.) in the country of their host organization for more than 12 months in the 3 years immediately prior to the reference date. Compulsory national service and/or short stays such as holidays are not taken into account. As far as international European interest organizations or international organizations are concerned, this rule does not apply to the hosting of eligible researchers. However, the appointed researcher must not have spent more than 12 months in the 3 years immediately prior to their recruitment at the host organization.
Selection process:
  • Each application will be screened by the respective supervisors from the host organizations
  • Prospective candidates will be contacted by the supervisors for individual interviews and the best ones will be shortlisted
  • The shortlisted candidates will be interviewed by the recruitment commission either in person or by SKYPE/Zoom
  • The candidates will be informed by e-mail about the results of their applications
How to apply
  • prepare your profile and provide sufficient details about your educational and work background, proofs of your education (or expected time of your MSc/diploma), your CV, and a motivation letter;
  • submit your application to recruit at ai-dd.eu before the deadline of March 1st, 2021 (the screening will start immediately; do not wait until the deadline to submit your application). Indicate ESR number in the title of the letter.
 
Pavel V. Karpov
added a project goal
The European Marie Skłodowska-Curie Innovative Training Network Advanced machine learning for Innovative Drug Discovery (AIDD) brings together 11 academic institutions and 4 international companies and is supported by 12 partner organisations.
An application of Artificial Intelligence in drug development is not straightforward and requires excellent knowledge of chemistry and biology. The main goal of the AIDD project is to train and prepare a new generation of scientists who have skills both in machine learning and in chemistry and can advance medicinal chemistry and speed up the drug development process.
The project offers 15 Ph.D. positions. Fellows employed will be supervised by academics who have excellent complementary expertise and contributed some of the fundamental AI algorithms which are used billions of times per day in the world, and leading EU Pharma companies who are in charge of new medicine and public health. All developed methods can be used individually but will also contribute to an integrated "One Chemistry" model that can predict outcomes ranging from different properties to molecule generation and synthesis. The network will offer comprehensive, structured training through a well-elaborated Curriculum, online courses, and six Schools.
Every fellow will work 1.5 years in an academic institution and 1.5 years in a company.
For further information see the site of the Project: http://ai-dd.eu