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Josip V. - Fullstack Developer, LLaMA, Machine Learning

Being part of Softaims has allowed me to see the full spectrum of what technology can achieve when guided by empathy, discipline, and creativity. Each assignment, regardless of size, represents an opportunity to bring clarity to complexity and to turn ambitious ideas into tangible outcomes. I’ve come to realize that successful development isn’t just about writing code—it’s about listening carefully, understanding deeply, and designing thoughtfully. Every client brings unique challenges, and I make it a priority to align my work with their goals, ensuring that the end result is both effective and lasting. Softaims fosters an environment where collaboration is not optional—it’s essential. The collective expertise within the team pushes me to think beyond conventional boundaries, to question, refine, and innovate. I believe that this process of shared learning and experimentation is what makes our solutions resilient and impactful. My ultimate goal is to build technology that feels effortless to use yet powerful in function. I approach every task with the mindset that small details can make a big difference. Through continuous refinement and dedication, I aim to contribute to the kind of work that not only serves today’s needs but anticipates tomorrow’s possibilities.

Main technologies

  • Fullstack Developer

    3 years

  • Deep Learning

    2 Years

  • PyTorch

    2 Years

  • Python Scikit-Learn

    2 Years

Additional skills

  • Deep Learning
  • PyTorch
  • Python Scikit-Learn
  • XGBoost
  • Matplotlib
  • Artificial Intelligence
  • Biostatistics
  • Git
  • Medical Writing
  • Research Papers
  • LangChain
  • OpenAI API
  • GPT API
  • Large Language Model
  • LLaMA
  • Machine Learning

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Potentially possible

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Experience Highlights

Codellama2-7b on Streamlit

Deploy local codellama-7b model on Streamlit. Utilizing: - Codellama - Langchain - Streamlit

Voice Chat with Research Paper

Streamlit app that allows users to voice chat with an uploaded PDF. Utilizing OpenAI and Cohere's API's. Specifically, using Whisper for speech to text, and an LLM from Cohere for semantic search and question answering

Google scholar account

List of research projects and citations.

Breast Cancer Lymph Node Classification

My PhD thesis project. The goal was to build/apply an explainable machine learning model for breast cancer lymph node classification, but only utilizing clinicopathological features.

The Role of AI in Breast Cancer Lymph Node Classification

Breast cancer affects countless women worldwide, and detecting the spread of cancer to the lymph nodes is crucial for determining the best course of treatment. Traditional diagnostic methods have their drawbacks, but artificial intelligence techniques, such as machine learning and deep learning, offer the potential for more accurate and efficient detection. Researchers have developed cutting-edge deep learning models to classify breast cancer lymph node metastasis from medical images, with promising results. Combining radiological data and patient information can further improve the accuracy of these models. This review gathers information on the latest AI models for detecting breast cancer lymph node metastasis, discusses the best ways to validate them, and addresses potential challenges and limitations. Ultimately, these AI models could significantly improve cancer care, particularly in areas with limited medical resources

Education

  • Split, Croatia; University of Split School of Medicine (USSM) Address Šoltanska 2, 21000, Split, Croatia Website http

    Doctor of Medicine (MD) in

    2013-01-01-2019-01-01

  • Berlin, Germany; International University of Applied Sciences

    Master's degree in Artificial Intelligence

    2022-01-01-2023-01-01

Languages

  • English

Personal Accounts