Maskinlæringsalgoritmer Jobs

I have a collection of scientific articles in text form and I want a clear, reproducible way to uncover the key themes and topics running through them. The core of the task is natural-language processing: ingest the articles, clean and normalize the text, then apply topic-modeling or similar techniques to surface the dominant concepts. My preference is a Python-based workflow—think spaCy, NLTK, gensim, BERTopic, or comparable libraries—but I’m open to whatever stack you feel will give the most coherent results. Visual summaries such as word clouds or interactive topic maps are definitely welcome if they help make the findings intuitive. Deliverables • Well-commented code or notebook that I can run locally without tweaks • A short methodological write-up ...

Halo! Saya sedang mencari tutor dari Indonesia yang memahami Process Mining, Bayesian Network, dan Random Forest untuk membimbing pengolahan data tesis saya. Saya memiliki paper referensi yang akan menjadi acuan utama. Peran tutor adalah membaca paper tersebut dan mengarahkan saya langkah demi langkah sesuai framework yang digunakan. Saya akan mengerjakan prosesnya sendiri, kemudian tutor membantu melakukan review dan koreksi jika terdapat kesalahan. Setelah itu, tutor juga membimbing saya dalam implementasi Random Forest hingga tahap olah data selesai. Ruang Lingkup Pekerjaan: - Membaca dan memahami paper referensi yang disediakan. - Mengarahkan langkah-langkah analisis sesuai framework pada paper. - Mereview dan memberikan masukan terhadap hasil olah data yg saya lakukan - Sesi diskusi...

Our client runs a call center with Nigerian agents calling US businesses. They need a Python script that makes the agents' accent sound more neutral and clear to American ears in real time during live phone calls. The hard part is already done. There is a fully built Node.js server that handles the live call audio. We just need you to write one Python script that plugs into it. What the script needs to do: Receive live audio from the server, process it through a voice conversion model to soften the Nigerian accent, and send the processed audio back. The whole process must happen in under 150ms so the conversation feels natural with no delay. Tech details: Audio comes in as mulaw 8kHz chunks. You can resample internally for better model performance then convert back before sending ...