Undergraduate Student

hace 1 semana

Barcelona, Barcelona, España Barcelona Supercomputing Center (BSC) A tiempo completo

Job Reference:

  • 356_24_CASE_WP_R0
    Position:
  • Undergraduate Student
  • ML-Aided Mathematical Optimization (MAMO)
    Closing Date:
  • Sunday, 16 June, 2024
    Reference: 356_24_CASE_WP_R0
    Job title: Undergraduate Student
  • ML-Aided Mathematical Optimization (MAMO)
    About BSC
  • The Barcelona Supercomputing Center
  • Centro Nacional de Supercomputación (BSC-CNS) is the leading supercomputing center in Spain. It houses MareNostrum, one of the most powerful supercomputers in Europe, was a founding and hosting member of the former European HPC infrastructure PRACE (Partnership for Advanced Computing in Europe), and is now hosting entity for EuroHPC JU, the Joint Undertaking that leads largescale investments and HPC provision in Europe. The mission of BSC is to research, develop and manage information technologies in order to facilitate scientific progress. BSC combines HPC service provision and R&D into both computer and computational science (life, earth and engineering sciences) under one roof, and currently has over 900 staff from 55 countries.
  • Look at the BSC experience:
  • BSC-CNS YouTube Channel
  • Let's stay connected with BSC Folks
    Context And Mission
  • In this topic a pure binary integer combinatorial optimization problem (ILP) is used to solve a fundamental question in Natural Language Processing (NLP): how to best compare and match the semantic contents of two natural language texts or "textual summaries" A and B, based on a given measure of the semantic similarity between their unit semantic constituents (USCs) ?
  • The student intern will focus on how to reduce the computational cost incurred when resolving pure binary integer linear programming (ILP) problems. The approach will consist in porting existing Python code to a high performance computing node partition, possibly using the PyCOMPSs framework for orchestration in distributed compute environments, and reducing the set of feasible ILP solutions by means of reinforced learning.
    Key Duties
  • Become familiar with several concepts: NLP, the measure of semantic similarity, the extraction of unit semantic constituent from unstructured text, the ILP formulation. The student will also establish a baseline workflow with Python, based on a conventional ILP solver applied to our joint setpacking and setpartitioning problem, using one or more FOSS ILP Python packages, and following the already formulated constraints of the ILP problem.
  • Design a semantically informed, stateoftheart Machine Learning method to reduce the set of feasible solutions for the initial semantic pairing ILP problem, whereby a reduced feasible set (or reduced feasible solutions space) is defined

as any possible ensemble of the most promising pairing combinations between semantic elements of text A and of text B, that satisfy the problem constraints.


  • Learn a generalizable model to infer acceptably reduced sets of feasible solutions for neverseenbefore textual summaries, establishing suitable
performance metrics.

  • Relax the pure binary integer constraint on decision variables (variables that can only accept values of either 0 or 1), to a ternary integer constraint (variables that can accept values of either 0 or 1, or 2), and to a pure integer constraint (variables can accept any integer values within the range [0, nB nA]) and will inspect obtained semantic similarity solutions from a natural (human) language
standpoint.

  • Prepare a situation and final report to be delivered on the last day of the student's interneship. The report should include (i) a structured bibliography on salient questions relevant to the technical work to carry out, (ii) a description of methodologies used throughout the interneship, (iii) any developped code, and (iv) results.

Requirements:

  • Education
  • Essential Knowledge and Professional Experience
  • Relevant scientific project experience gained through recent academic courses are positively valued in the following areas: computer science, mathematical analysis and optimization schemes
  • Additional Knowledge and Professional Experience
  • Fluent written and spoken English
  • Collaborative and version controlled work environments (e.g. gitlab, github, etc.)
  • Unix/Linux and any related scripting shell


 Python
  • Differential calculus, vector analysis, combinatorial optimization (at least the basic principles thereof), convexity in optimization problems
  • Knowledge of the concept of distributed computing
  • Knowledge of the difference between an AI instance and an ML model
  • Competences
  • Good communication and presentation skills.
  • Ability to work both independently and within a team.

Conditions

  • The position will be located at BSC within the CASE Department
  • We offer a fulltime contract (37.5h/week), a good working environment, a highly stimulating environment with stateoftheart infrastructure, flexible working hours, extensive training plan, restaurant tickets, private health insurance, support to the r

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