**Company: University of Tartu Job Description Summary: The University of Tartu is looking to hire a postdoctoral researcher for a five-year project focusing on long-term trends in industrial modernity. The researcher will work on tasks such as measuring continuities and ruptures in environmental and technoscientific ideas, developing text mining workflows, and creating an industrial modernity index. Applicants must have a Ph.D. in relevant fields and experience in text mining and natural language processing. The salary range is 2300-2500 EUR per month, and remote working arrangements can be negotiated. Job Description: Our team of interdisciplinary researchers is seeking to recruit (a) postdoctoral researcher(s) to join our five-year project. Deadline for applications: open until filled. Expected starting date: ideally autumn 2024, but January 2025 is also possible. Hiring capacity: up to 1.5 fte, including the possibility of full-time employment for one researcher. Remote working: this can be negotiated for researchers involved part-time; however, for full-time researchers working in Estonia is strongly preferred to facilitate teamwork. Salary range: 2300-2500 EUR per month, depending on the experience (including the possibility of annual raise). Note that the average salary in Estonia is currently around 1900 EUR. The study will focus on the identification of long-term trends in industrial modernity in a comparative-historical perspective, combining the text mining of digitalized newspapers with existing databases. The postdoctoral researcher(s) will work closely with the rest on the team on the following tasks: Measuring continuities and ruptures in fundamental environmental and technoscientific ideas, institutions and practices in G20 countries from 1900 to 2025; Contributing to the development of a text mining workflow for measuring long-term ideational change using digitized newspapers; Developing an industrial modernity index to identify countries currently most likely to enact a deep sustainability turn; Contributing to the development of a text mining workflow to compare ideational change in newspapers to countries’ World Values Survey scores.
Company:University of Tartu
Qualifications:Have a PhD in one of the following fields: sustainability transitions studies, science and technology studies, innovation studies, environmental studies, sustainability science, computational social science, digital humanities and a strong interest in sustainability issues. Have proven experience with at least two items from the following list and willingness to expand the skill-set as required: – Gathering and maintaining large digital corpora for text mining – Cultural analytic studies based on large text collections – Applying Natural Language Processing on historical OCR texts – Keyword extraction, topic modelling, sentiment analysis – Automatic content extraction and text classification – Time series analysis of large text collections – Large Language Models and zero-shot learning Are creative, independent, curious and willing to think big. An ambition to save the world would probably help too 😉
Educational level:Ph. D.
“Fine-tuning” means adapting an existing machine learning model for specific tasks or use cases. In this post I’m going to walk you through how you can fine tune a large language model for sentence similarity using some hand annotated test data. This example is in the psychology domain. You need training data consisting of pairs of sentences, and a “ground truth” of how similar you want those sentences to be when you train your custom sentence similarity model.
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Natural language processing What is natural language processing? Natural language processing, or NLP, is a field of artificial intelligence that focuses on the interaction between computers and humans using natural language. NLP is a branch of AI but is really a mixture of disciplines such as linguistics, computer science, and engineering. There are a number of approaches to NLP, ranging from rule-based modelling of human language to statistical methods. Common uses of NLP include speech recognition systems, the voice assistants available on smartphones, and chatbots.
