Job board

We at KTH AI Society strive to bridge the gap between our members and the industry. It’s quite simple, companies working for the solutions of tomorrow need the talent of today. So below we listed all relevant work opportunities for you to take on new challenges.

For more opportunities, visit our strategic partner, Stockholm AI.

If you want to make a job posting contact us at jobs@kthais.com.

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Master Thesis Project: Unified Mixed-Modal Transformers for Efficient Understanding and Generation

🌍 Modulai, Stockholm

Master thesis

Apply before 2024-10-25

Focus: Work on cutting-edge machine learning research at Modulai by developing unified mixed-modal models that combine data from multiple domains, such as text, images, video, and audio.

Key Responsibilities:

  • Model Development:
    Design and experiment with mixed-modal models that incorporate both autoregressive methods for text generation and diffusion-based techniques for visual data.

  • Data Fusion:
    Develop models capable of processing both discrete tokens for text and continuous/discrete vectors for visual data, performing tasks like text-to-image generation and visual question answering.

  • Performance Comparison:
    Compare the performance of the unified mixed-modal model against state-of-the-art multimodal models, focusing on overall performance, computational efficiency, and fine-tuning across domains.

Required Skills:
  • Completing a master’s degree in machine learning or a related field with coursework in machine learning and programming.
Tools and Techniques:
  • Transformer-based architectures
  • Diffusion models
  • Multimodal models
  • Python, PyTorch, Git
Application Requirements:
  • GitHub account (if available).
  • Grades from bachelor’s and master’s programs.
  • CV or LinkedIn profile.

Location:
Stockholm, Sweden

Application Deadline:
Submit your application by October 25th, but the process may close earlier if suitable candidates are identified.

Master Thesis Project: Large-to-Small Language Model Distillation

🌍 Modulai, Gothenburg

Master thesis

Apply before 2024-10-25

Focus: Research and develop efficient language models using knowledge distillation, transferring knowledge from large language models to smaller, more efficient models at Modulai.

Key Responsibilities:

  • Knowledge Distillation:
    Implement and experiment with knowledge distillation techniques, transferring knowledge from a large teacher model to a smaller student model.

  • Model Optimization:
    Optimize the student model to replicate the behavior of the teacher model with a focus on computational efficiency and maintaining model quality.

  • State-of-the-Art Research:
    Explore and apply the latest research in distillation methods, including the use of soft labels and model pruning, to improve the effectiveness of the student model.

Required Skills:
  • Completing a master’s degree in machine learning or a related field with coursework in machine learning and programming.
Tools and Techniques:
  • Transformer-based architectures
  • Knowledge distillation strategies
  • Python, PyTorch, Git
Application Requirements:
  • GitHub account (if available).
  • Grades from bachelor’s and master’s programs.
  • CV or LinkedIn profile.

Location:
Gothenburg, Sweden (On-Site)

Application Deadline:
Submit your application by October 25th, but the process may close earlier if suitable candidates are identified.

Master Thesis - Time Series Forecasting

🌍 Gilion, Stockholm

Master thesis

Focus: Advanced forecasting solutions for customer acquisition to aid data-driven business decisions.

Research Questions:
  • Trend Detection: How to react to market shifts, economic changes, and cyclical patterns in forecasting?
  • Limited Data: How to forecast in both established and new markets with limited historical data?
  • Lead Indicators: How to incorporate metrics (e.g., marketing spend) into forecasts?
  • Probabilistic Forecasting: How to quantify forecast uncertainty and improve reliability?
Methodology:
  • Techniques: Apply machine learning & deep learning for trend detection, anomaly detection, and scalable forecasting.
  • Tools: Investigate change point detection, compare methodologies with real-world datasets, and evaluate within Gilion's framework.
Expected Outcomes:
  • A robust, scalable forecasting solution for customer acquisition.
  • Documentation on methodologies and future improvement suggestions.
Required Skills:
  • Proficiency in Python & SQL
  • Experience with machine learning & deep learning (e.g., PyTorch, TensorFlow)
  • Familiarity with statistical forecasting tools
Master Thesis - Indicative Metrics of a Successful Market Launch

🌍 Gilion, Stockholm

Master thesis

Focus: Assess whether launching in new markets leads to increased sales and identify key metrics that distinguish successful market entries.

Research Questions:
  • Key Metrics: What transactional data points (e.g., daily revenue, customer acquisition, retention) signal a successful market launch?
  • Growth Timelines: What are the reasonable timeframes for growth after entering a new market?
Methodology:
  • Techniques: Analyze transactional data (e.g., from Fortnox or Apple App Store) using quantitative methods like time series analysis and linear regression.
  • Comparison: Evaluate historical performance of market launches for individual companies and across other companies.
Expected Outcomes:
  • A framework for evaluating the success of new market entries.
  • Insights to help determine whether a company's assumptions about growth in new markets are realistic.
Required Skills:
  • Experience working with data sets
  • Strong interest in market analysis and business expansion
  • Enthusiasm for cross-competency collaboration
Nice to Have:
  • Familiarity with data visualization tools (e.g., Data Studio, Google Sheets)
  • Experience with SQL