Forschungsbereich: Natural Language Processing

Eine Bewerbung in diesem Forschungsbereich ist zur Zeit nicht möglich. Ein Update der Themen zur Bewerbung wird vorrausichtlich im Sommersemester 2024 stattfinden.
Ansprechpartner: Philipp Haan

Forschungsbereich: Consumer Insights 

The Evolution of Job Roles in the Technological Era: Development of a Semi-automated Approach for Requirement and Responsibility Tracking

  • How can a semi-automated methodology be developed to track the emergence and evolution of job roles in response to technological advancements?
  • What are the key skills and technologies driving the development of emerging job roles, and how have these changed over time?
  • Can machine learning models predict future trends in job roles based on historical data and technological trajectories?

This topic delves into the evolution of job roles in response to technological advancements. A semi-automated approach is proposed to analyze large datasets containing job descriptions, skills requirements, and technological keywords. The thesis aims to provide insights into the changing landscape of job markets, helping educational institutions and job seekers prepare for future skill demands.

Allgemeine Anforderungen:

  • Proficiency in Natural Language Processing (NLP) for job description analysis.
  • Knowledge of machine learning algorithms for trend prediction.
  • Familiarity with theories on job market evolution and technological innovation.

Empfohlene Studienstufe: Master
Zeitrahmen: ab Juli/August 2024
Betreuerin: Natalie Rubin

Bridging the Gap: A Semi-Automated Analysis of Skills Mismatch in Education and Industry

  • How can a semi-automated approach be developed to analyze skills mentioned in job postings and align them with educational program curricula?
  • What are the prevailing patterns of skills mismatch over time, and how do they vary across industries?
  • Can machine learning models predict potential skills gaps based on current industry trends and educational offerings?

The thesis explores the persistent issue of skills mismatch between education and industry demands. A semi-automated methodology is proposed to bridge this gap by aligning the skills emphasized in job postings with the skills taught in relevant educational programs. The goal is to provide insights into improving educational curricula and addressing the dynamic needs of the job market.

Allgemeine Anforderungen:

  • Familiarity with Natural Language Processing (NLP) and its application areas.
  • Basic knowledge on labor market frameworks and dynamics.
  • Knowledge on statistical data analysis and forecasting.

Empfohlene Studienstufe: Master
Zeitrahmen: ab Oktober/November 2024
Betreuerin: Natalie Rubin

Forschungsbereich: Strategic Foresight

Eine Bewerbung in diesem Forschungsbereich ist zur Zeit nicht möglich. Ein Update der Themen zur Bewerbung wird vorrausichtlich im Sommersemester 2024 stattfinden.
Ansprechpartner: Philipp Haan