u/SpectreMold

Ever since college, I’ve realized I subconsciously frame different chapters of my life like a shonen anime.

Whenever I’m going through a major transition:college, grad school, an internship, or starting a full-time job, I’ll hear an upbeat track (either an actual anime OP like MHA or a pop-punk song) and instantly map out an opening sequence in my head.

The "setting" becomes whatever life stage I'm currently in, and the cast features the actual people around me at the time: professors, classmates, friends, and coworkers as the allies, mentors, or rivals. The music syncs up with different mental cuts of us grinding through tough projects, hanging out, or tackling milestones together.

It’s almost like my brain’s way of processing life transitions and building up motivation through auditory framing.

I do this with anime EDs too. When I am walking home after a long day or having a quiet moment to reflect, my brain switches from the high energy OP to a chill ending sequence set to a Japanese track or a softer song. It is like the mental cooldown after a long episode of work or classes.

Does anyone else do this with specific genres or songs to set the scene for their actual life? What track is currently playing for your current chapter?

Hey everyone,

I’m finishing up my master’s in physics with a concentration in astrophysics, and I most recently worked as an imaging geophysicist, which is a role that involved signal processing knowledge. However, I did not have a passion or curiosity for geoscience. I’m looking to break into another role for signal processing.

When I look at google and LinkedIn job postings I can't seem to find many entry level roles. For those already in the field, how was your experience finding an entry-level signal role? Are there any specific industries that tend to have more opportunities for physics degree holders early in their careers? Is there much upskilling I need to do here? I am hoping the answer to the last question is no, because I was previously considering DS/ML roles, but a professional in these areas mentioned to me that I would not be competitive since I lack SWE, MLOps, Docker, Cloud, etc. skills not taught in a physics education or used in physics research (hence why now I am thinking about SP).

Any advice on job search strategies, good companies to look at, or must-have skills would be really appreciated. I am a US citizen (but I do not hold active security clearance yet) so I am considering jobs in the DC-Baltimore metro area; do I stand a competitive chance here?

Thanks

I have a physics MSc and most recently worked as a geophysicist where I was applying signal processing skills, but ultimately my heart was not in the geoscience domain.

I am curious to know what types of signal processing jobs are out there that I could apply to? For example, there is "DSP engineer" that I saw for defense contractors in the DC metro.

I asked Gemini this question which gave me this list, but I would like to hear from a human being:

>Digital Signal Processing (DSP) Engineer, DSP Algorithm Developer, Embedded DSP Software Engineer, Real-Time Signal Processing Engineer, Audio DSP Engineer, Radar / RF Signal Processing Engineer, Biomedical Signal Processing Engineer, Computer Vision Engineer, Wireless Communications Engineer, FPGA/DSP Hardware Engineer, Sensor Fusion Engineer, Machine Learning & DSP Specialist, Communication Systems Engineer

Also, would I need to do massive up-skilling in general for signal processing, or would I already be competitive for positions given my background? I am very experienced with Python, and I have coded in C++ and MATLAB before too. I have also used ML algorithms for physics research projects.

I am in my mid-20s with a physics MSc and most recently worked as a geophysicist where I was applying signal processing skills. I have a growing interest in ML/AI and have seriously considered a career in this area as a lucrative pivot outside of physics (in physics, we are dealing with unemployment/underemployment issues). I have used ML algorithms for analyzing data in past physics research projects, but unfortunately:

1. I lack work experience/internships in ML/data.
2. I lack SWE skills and do not have much experience with Git, cloud, MLOps, Docker, APIs, etc.

It seems to me like even 2026 data science job postings converge with MLE are asking for these skills, but they are not taught in my physics education sadly.

Because of this, I have doubts about my previous plan to gun for an entry-level data scientist position first before transitioning to ML. I also considered first getting a data analyst role, but I think I would feel frustrated by the lack of math/programming (I heard they are mainly SQL and dashboarding) and the massive competition with recent college graduates for these roles. However, what I am thinking is leveraging my background first by entering a signal processing engineer or related role, and then transitioning from an SP role into an ML role. So essentially:

Signal Processing Engineer -> Data Scientist / MLE / CV Engineer

Is this a good idea for a transition? Is it true that signal processing and ML have overlap, and thus I should aim for ML from this angle rather than the DA -> DS -> ML path?

I am about to leave Singapore soon and I found someone to potentially takeover my tenant. Before approval, my landlord simply asked to share his "Profile".

What details does this mean exactly? Ethnicity? Occupation? Nationality?

I am foreigner that currently lives in a studio HDB in Serangoon, but I will have to leave the country July 25. I plan to handover the HDB to a replacement tenant but before doing so, I need to have the apartment cleaned. I plan to move into a hotel/aparthotel during the last few days of my stay to make sure all my belongings are taken out of the apartment and the folks can clean it.

Does anyone have any suggestions for professional cleaning services for my situation.?

I am a physics master's degree holder with research experience in astrophysics and most recently worked in industry as an imaging geophysicist. Although I have enjoyed learning physics in high school and college, long term my goal is to do applied, production ML/AI (data scientist, ML engineer, AI engineer, etc.)

How difficult/easy is it for me to pivot from my background to these roles in 2026? I feel these roles have strong alignment with my interests and career goals, and I have programming and ML experiences from physics research projects, but I also feel I will have to do considerable self-study as job descriptions in 2026 now ask for a couple things not taught in a physics degree (version control, MLOps and containerization, cloud architecture, software engineering principles like OOP, RAG, you can tell me more). Of course, I am more than willing to put in the effort to learn these, but will it be enough in combination with my background to convince employers? Especially if I do not have internship experiences (since I spent my summers doing physics research projects).

Additionally, in my last role as a geo, there was not an avenue to incorporate programming nor ML algorithms in the work, as the work was done 100% through proprietary software.

We see in the news students receiving these types of emails, but what about faculty? If so, what types of emails do you see?

I am a mathematics tutor and I am about to tutor a student in Calc 1. The student did not perform well in his first attempt at the course, and is looking to get an assessment on his level with calc and prerequisites, like algebra and trigonometry.

Does anyone know free, online resources I can point him to to diagnose his current level?

I don't understand something about myself. I loved learning physics in high school and college, and I still enjoy tutoring physics. But when I worked as an astronomy research student or as an industry geophysicist, I did not enjoy those jobs. They felt much more cognitively demanding in a way that was frustrating rather than engaging.

What I find difficult about those kinds of physics jobs is that when I see something unusual in an astrophysics image, a stellar spectrum, or a seismic trace, there are so many possible explanations to consider. It could be an instrument issue, a data collection problem, a physical effect I have not learned yet, or even a concept I once knew but have forgotten. Astrophysics and geophysics are such vast fields that I often feel like I am spinning my wheels trying to think through every possible explanation over everything I see. The cognitive load becomes overwhelming, and I end up feeling paralyzed, procrastinating, and becoming very stressed whenever I try to figure it out.

By contrast, even though I was a physics major, I think I would rather work on problems involving logic, optimization, or structured troubleshooting over the long term. Those problems feel less stressful to me, or at least they involve a kind of stress that I handle better. The issues are often more well defined, more commonly encountered, and there are usually established approaches for solving them. There are open source tools, documentation, online discussions, and increasingly LLMs that can help point me in the right direction.

For example, if a dataset has missing values, I can think of several ways to address that. If a model fit does not converge, I have ideas for how to troubleshoot it. Those problems feel more bounded and actionable.

Does this way of thinking make sense? Is there any psychological research or theory that would explain why I enjoy learning and teaching physics but find open ended scientific research and interpretation much more stressful?

I (26M) am a US citizen based in Singapore and will be visiting Bali next weekend. I am staying near the airport.

Is it worth shilling over S$140 dollars to pay for a Ubud private tour, or am I better off traveling to these places by a Grab motorbike to explore the places myself?

The places for the tour is (travel and all admissions included)

• Sacred Monkey Forest Sanctuary
• Tegalalang Rice Terrace
• d'Alas Restaurant
• Tirta Empul Temple
• Tegenungan Waterfall
• Ubud Art Market
• Ubud Palace

I am a geophysicist with a physics master's degree and I actually pivoted previously from astrophysics. My goal is to pivot to a career in machine learning and in order to meet and network with people, I have considered attending a conference in person. However, given that I currently do not use ML in my current role and am only been involved in ML from past coursework/physics research projects/portfolio projects on the side, how do I go about networking and having conversations?

In addition, how can I attend a conference without missing my work on weekdays?

https://preview.redd.it/0jjo318rotxg1.jpg?width=2048&format=pjpg&auto=webp&s=77aca7ce3d3c5f5e6df93c80d0ac0d8315c126e2

I can get AI to slim my body but at the expense of giving me a face that does not look like me. Help is appreciated!